Image pixel data processing method and device, display and medium

Abstract

The application is suitable for the technical field of image processing, and provides a processing method and device of image pixel data, a display and a medium, the method comprises the following steps: acquiring pixel data of each pixel in a to-be-displayed image, the pixel data comprising: gray scale values of each sub-pixel of the pixel; performing abnormality identification on the to-be-displayed image based on the gray scale values of each sub-pixel of each pixel, to obtain an image abnormal area of the to-be-displayed image; performing gray scale compensation on at least part of the gray scale values in the image abnormal area according to a target compensation mode, the target compensation mode comprising: reducing high gray scale pixels and / or raising low gray scale pixels; and displaying the to-be-displayed image based on the updated gray scale values of each sub-pixel of each pixel. The method can effectively solve the problem of abnormal display of the image abnormal area by performing gray scale compensation on at least part of the gray scale values in the identified image abnormal area according to the target compensation mode, and improve the application range of image processing.

Description

Technical Field

[0001] This application belongs to the field of image processing technology, and in particular relates to a method, apparatus, display and medium for processing image pixel data. Background Technology

[0002] Thin Film Transistor Liquid Crystal Display (TFT-LCD) is a type of liquid crystal display in which each pixel is driven by a thin film transistor integrated behind the pixel.

[0003] TFT-LCD displays primarily rely on the difference in voltage across a capacitor to function. However, the common electrode connected to one end of the capacitor is susceptible to interference from the data line responsible for transmitting the polarity of the capacitor voltage, resulting in fluctuations in the common voltage and consequently affecting the normal display of the actual image.

[0004] To address the aforementioned issues, existing technologies primarily prevent abnormal display by altering the polarity reversal method. However, these solutions have limited applicability and cannot effectively resolve abnormal display issues in certain special types of screens (such as string-type displays). Summary of the Invention

[0005] This application provides a method, apparatus, display, and medium for processing image pixel data, which can solve the problem that the prior art cannot effectively solve some special abnormal display problems, and improve the applicability of image processing.

[0006] In a first aspect, embodiments of this application provide a method for processing image pixel data, including:

[0007] Obtain the pixel data of each pixel in the image to be displayed. The pixel data includes the grayscale values ​​of each sub-pixel of the pixel.

[0008] Based on the grayscale values ​​of each sub-pixel of each pixel, anomaly identification is performed on the image to be displayed to obtain the abnormal regions of the image to be displayed.

[0009] According to the target compensation method, at least some grayscale values ​​in the abnormal area of ​​the image are compensated for grayscale values ​​to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method includes: reducing high grayscale pixels and / or increasing low grayscale pixels.

[0010] The image to be displayed is based on the updated grayscale values ​​of each sub-pixel of each pixel.

[0011] Secondly, embodiments of this application provide an image pixel data processing apparatus, including:

[0012] The acquisition module is used to acquire pixel data for each pixel in the image to be displayed. The pixel data includes the grayscale values ​​of each sub-pixel of the pixel.

[0013] The anomaly detection module is used to detect anomalies in the image to be displayed based on the grayscale values ​​of each sub-pixel of each pixel, and to obtain the abnormal regions of the image to be displayed.

[0014] The grayscale compensation module is used to perform grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method, so as to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method includes: reducing high grayscale pixels and / or increasing low grayscale pixels.

[0015] The display module is used to display the image to be displayed based on the updated grayscale values ​​of each sub-pixel of each pixel.

[0016] Thirdly, embodiments of this application provide a display including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any of the first aspects.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects.

[0018] Fifthly, embodiments of this application provide a computer program product that, when run on a display, causes the display to perform the method described in any one of the first aspects above.

[0019] This application provides a method, apparatus, display, and medium for processing image pixel data. The method includes: acquiring pixel data of each pixel in an image to be displayed, the pixel data including grayscale values ​​of each sub-pixel of the pixel; performing anomaly identification on the image to be displayed based on the grayscale values ​​of each sub-pixel of each pixel to obtain an image anomaly region of the image to be displayed; performing grayscale compensation on at least a portion of the grayscale values ​​in the image anomaly region according to a target compensation method to obtain updated grayscale values ​​of each sub-pixel of each pixel, the target compensation method including: reducing high grayscale pixels and / or increasing low grayscale pixels; and displaying the image to be displayed based on the updated grayscale values ​​of each sub-pixel of each pixel. By using the above technical solution, and by performing grayscale compensation on at least a portion of the grayscale values ​​in the identified image anomaly region according to a target compensation method, the problem of abnormal display in image anomaly regions can be effectively solved, and the applicability of image processing can be improved. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1a This is a schematic diagram of the driving mechanism of an internal circuit of a TFT-LCD according to an embodiment of this application;

[0022] Figure 1b This is a simplified schematic diagram of a sub-pixel provided in an embodiment of this application;

[0023] Figure 1c This is a schematic diagram illustrating the relationship between display data and a common electrode provided in one embodiment of this application;

[0024] Figure 1d This is a schematic diagram illustrating a point reversal method for the polarity reversal of the entire image, provided in an embodiment of this application.

[0025] Figure 1e This is a schematic diagram illustrating another polarity reversal method for the entire image provided in an embodiment of this application, namely point reversal;

[0026] Figure 1f This is a schematic diagram illustrating a polarity reversal method for the entire image as a 1+2 line reversal, provided in one embodiment of this application;

[0027] Figure 1g This is a schematic diagram of a special screen provided in an embodiment of this application;

[0028] Figure 2 This is a schematic flowchart of an image pixel data processing method provided in an embodiment of this application;

[0029] Figure 3a This is a flowchart illustrating a method for processing image pixel data according to another embodiment of this application;

[0030] Figure 3b This is a schematic flowchart of another image pixel data processing method provided in an embodiment of this application;

[0031] Figure 3c This is a schematic diagram of a screen showing a string displayed normally, provided in an embodiment of this application;

[0032] Figure 3d This is a schematic diagram of a screen showing an abnormal string display according to an embodiment of this application;

[0033] Figure 4This is a structural block diagram of an image pixel data processing apparatus provided in one embodiment of this application;

[0034] Figure 5 This is a schematic diagram of the structure of a display provided in one embodiment of this application. Detailed Implementation

[0035] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0036] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0037] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0038] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0039] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0040] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0041] It can be argued that one of the advantages of LCD monitors compared to traditional cathode ray tube (CRT) and plasma displays is their energy efficiency. For example, the power consumption of an LCD monitor is only half that of a CRT monitor of the same size, and even lower than that of a plasma monitor. Furthermore, LCD monitors are more environmentally friendly than traditional CRT monitors because they do not contain high-voltage components, thus avoiding the risk of excessive radiation due to high voltage. Additionally, the display area itself does not emit radiation; only the driving circuitry emits a small amount of electromagnetic waves. Strictly sealing the casing can reduce electromagnetic interference (EMI). Therefore, the radiation levels of LCD monitors are generally lower than those of CRT monitors.

[0042] Furthermore, LCD monitors offer a large viewable area. Specifically, LCDs achieve their display purpose primarily by controlling the state of liquid crystal molecules through electrodes on the screen. Even with a larger screen, the overall volume doesn't increase proportionally (e.g., only the size increases without increasing the thickness, hence many products offer wall-mounting functionality to save space). Moreover, LCD monitors are lighter than traditional monitors of the same display area; for example, an LCD TV weighs about one-third of a traditional TV. Therefore, LCD monitors are also known as "cool displays" or "eco-friendly displays." Consequently, current development of LCD monitors is moving towards higher resolution, higher image quality, and larger screen sizes.

[0043] Thin film transistor liquid crystal display (TFT-LCD) is a type of liquid crystal display. Each pixel on this type of display is mainly driven by thin film transistors integrated behind the pixel, and the driving method of TFT-LCD is line-by-line scanning. Figure 1a This is a driving schematic diagram of the internal circuit of a TFT-LCD provided in one embodiment of this application, as shown below. Figure 1a As shown, the left Gate terminal can be considered as the switching signal for each thin-film transistor (TFT). For example, when the Gn signal is high, the TFT corresponding to that row is turned on, and the display data in the TFT column direction (such as...) Then it can be written into the corresponding sub-pixel.

[0044] Figure 1b This is a simplified schematic diagram of a sub-pixel provided in an embodiment of this application, as shown below. Figure 1b As shown, it may include a control switch T, a liquid crystal capacitor Clc, a storage capacitor Cst, a G1 line responsible for transmitting switch signals, and an S line responsible for transmitting display data. One end of the liquid crystal capacitor Clc and the storage capacitor Cst can be a pixel electrode, and the other end can be a common electrode (VCOM).

[0045] Because LCD monitors use a capacitor structure for charging and discharging, using a DC circuit to drive them will result in residual charge at the capacitor's ends, manifesting as image retention during display. To avoid this, the DC circuit can be replaced with an AC circuit. LCD monitors primarily function by utilizing the different voltages across the capacitors, which in turn affect the amount of light transmitted. Figure 1c This is a schematic diagram illustrating the relationship between display data and the common electrode provided in an embodiment of this application, as shown below. Figure 1c As shown, the displayed data (such as...) The waveform shown in the diagram () can be interpreted as a pulse waveform. Because a capacitor is connected between the data line responsible for transmitting display data and the common electrode, the common electrode VCOM is susceptible to interference from the data line. Compared to the optimal VCOM, VCOM exhibits voltage jitter, which causes abnormalities such as crosstalk in the display. Furthermore, during the H-active time period... Data can be written to the corresponding sub-pixel, but no data is written to the sub-pixel during the H-blank time period (i.e., the blank time period).

[0046] Specifically, interference with the common electrode VCOM is mainly due to the cumulative effect of all positive and negative data lines within a row on VCOM when the display screen shows one or more rows. The data in the current row generally only affects the data in that row and the data before it. If the polarity of the data line is +, the VCOM voltage will fluctuate upwards; if the polarity of the data line is -, the VCOM voltage will fluctuate downwards. This fluctuation will cause the screen display to be too bright or too dark, affecting the actual display of the image and thus leading to poor monitor quality.

[0047] Figure 1d This is a schematic diagram illustrating a point reversal method for polarity reversal of the entire image, as provided in an embodiment of this application. Figure 1dAs shown, "±" can be understood as the voltage of the subpixel relative to the VCOM voltage; higher than VCOM voltage is "+", and lower is "-". The black portion represents the effect of the "H" string on the display screen. Because the black portion has a negligible impact on VCOM, it is not needed when calculating crosstalk; only the bright portion needs to be calculated. Taking the first subpixel as "+", the overall polarity of the second row is (8+10-)*2. Therefore, the second row's data has a bias towards "-" on VCOM. The display of other positions in the second row will be affected by the "H" string, and the more "H" strings there are, the more severe the bias towards "-". Here, 2 represents the number of "H" strings.

[0048] To address the aforementioned issues, existing solutions typically employ a screen detection mechanism. The principle is to detect the display of a certain undesirable pattern and then change the inversion method to prevent its generation. Changing the inversion method could involve altering the polarity of the displayed data in the data cable from one of the polarities listed in Table 1 to another. Table 1 shows the various polarities of the displayed data in the data cable. The polarity can be determined by the ratio of the sub-pixel voltage to the VCOM voltage; if the sub-pixel voltage is higher than the VCOM voltage, the polarity is "+", and if the sub-pixel voltage is lower than the VCOM voltage, the polarity is "-".

[0049] Table 1 shows the various polarities of the data in the data lines.

[0050]

[0051] However, the image detection mechanism has two drawbacks. First, it can only detect the smallest detection unit. Second, for some special images, even after changing the polarity using the image detection mechanism, it is still unable to satisfy the problem of canceling out the positive and negative polarities, that is, it still cannot solve the problem of crosstalk and other abnormalities in the image.

[0052] Figure 1e This is a schematic diagram illustrating another method of polarity reversal for the entire image, namely dot reversal, provided in an embodiment of this application. Figure 1e As shown, regarding the polarity problem in the case of reversal for each "H" character, when the polarity reversal method is dot reversal (i.e., the polarity between adjacent sub-pixels is opposite), the polarity of each H is asymmetrical, and the degree becomes more and more serious as the number of strings increases.

[0053] Figure 1f This is a schematic diagram illustrating a polarity reversal method for the entire image as a 1+2 line reversal, provided in one embodiment of this application. Figure 1fAs shown, regarding the polarity problem under the inversion of each "H" character, when the polarity inversion method is 1+2 line inversion (i.e., one plus two line dot inversion), the polarity of each H is still asymmetrical, and the degree becomes more and more serious as the number of strings increases.

[0054] For example, Figure 1g This is a schematic diagram of a special screen provided in an embodiment of this application, such as... Figure 1g As shown, a special screen can contain an area displayed as a string (i.e., an "abnormal area"). Such an area can be composed of the string "H" from a text software. In this case, the display of other locations in the same row as this area (i.e., "abnormal areas") will be affected by the "H" string, thus affecting the display of the actual screen.

[0055] Based on this, embodiments of the present invention provide a method for processing image pixel data, which can improve the crosstalk phenomenon caused by the above-mentioned positive and negative polarity asymmetry and improve the product quality of the display.

[0056] Figure 2 This is a schematic flowchart of an image pixel data processing method provided in an embodiment of this application. It is an example and not a limitation, and the method can be applied to a display.

[0057] S101. Obtain the pixel data of each pixel in the image to be displayed. The pixel data includes the grayscale value of each sub-pixel of the pixel.

[0058] In this context, the image to be displayed can be considered as the image frame to be displayed, and each pixel in the image frame can refer to the smallest display unit of the display screen, such as a thin-film transistor. In this embodiment, each pixel can be composed of three sub-pixels: red, green, and blue (RGB), to present many different colors. The light source behind each sub-pixel can display different brightness levels, and the grayscale value represents different brightness levels from the darkest to the brightest. Furthermore, each pixel on the LCD screen is composed of red, green, and blue at different brightness levels, ultimately forming different colors.

[0059] Therefore, this embodiment can acquire pixel data for each pixel in the image to be displayed, and perform subsequent processing steps based on the acquired pixel data to eliminate crosstalk that may exist in the image. The pixel data may include the grayscale values ​​of each sub-pixel of the pixel, or other data related to the pixel, etc., which are not limited in this embodiment.

[0060] S102. Based on the grayscale values ​​of each sub-pixel of each pixel, perform anomaly identification on the image to be displayed to obtain the abnormal regions of the image to be displayed.

[0061] An abnormal region in an image can refer to an area in the image to be displayed that exhibits abnormalities, such as an area where crosstalk occurs.

[0062] After obtaining the grayscale values ​​of each sub-pixel of each pixel, anomaly recognition can be performed on the image to be displayed based on the obtained grayscale values ​​of each sub-pixel of each pixel to obtain the abnormal regions of the image to be displayed. The means of anomaly recognition are not limited. For example, anomaly recognition of the image to be displayed can be performed by a preset recognition model. That is, the grayscale values ​​of each sub-pixel of each pixel can be input into the preset recognition model to output the abnormal regions of the image to be displayed. The preset recognition model can be a model that has been trained on a neural network model based on the grayscale values ​​and abnormal regions of multiple sample images in advance, and can be used to perform anomaly recognition on the image.

[0063] Alternatively, abnormal regions of the image to be displayed can be determined by identifying the grayscale values ​​of each sub-pixel. For example, if a region is found to have only two grayscale values ​​for its sub-pixels, it can be considered an abnormal region. Furthermore, abnormal regions of the image to be displayed can be identified by calculating the grayscale values ​​of each sub-pixel of each pixel. For example, the difference between the maximum and minimum grayscale values ​​of a sub-pixel in a region can be calculated, and the magnitude of the difference can be used to determine whether the region is an abnormal region.

[0064] S103. Perform grayscale compensation on at least some grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method includes: reducing high grayscale pixels and / or increasing low grayscale pixels.

[0065] The target compensation method can refer to the method of grayscale compensation for abnormal areas of an image. For example, the target compensation method can include reducing high grayscale pixels, that is, reducing the grayscale of sub-pixels with higher grayscale values ​​in the abnormal area of ​​the image. The target compensation method can also include increasing low grayscale pixels, that is, increasing the grayscale of sub-pixels with lower grayscale values ​​in the abnormal area of ​​the image. The target compensation method can also include reducing high grayscale pixels and increasing low grayscale pixels, that is, reducing the grayscale of sub-pixels with higher grayscale values ​​in the abnormal area of ​​the image while increasing the grayscale of sub-pixels with lower grayscale values ​​in the abnormal area of ​​the image.

[0066] There are no restrictions on the specific method for determining the target compensation method. For example, it can be determined based on the specific content of the abnormal area in the actual image, or it can be determined directly based on the monitor. Different monitor models or types can correspond to different target compensation methods.

[0067] Specifically, this step can perform grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method, thereby obtaining the updated grayscale values ​​of each sub-pixel of each pixel. The specific process of grayscale compensation can be determined according to actual needs, such as depending on the target compensation method. For example, when the target compensation method is to reduce high grayscale pixels or increase low grayscale pixels, grayscale compensation can be performed on a portion of the grayscale values ​​in the abnormal region of the image, such as only reducing the grayscale values ​​of the sub-pixels with higher grayscale values ​​in the abnormal region of the image. When the target compensation method is to reduce high grayscale pixels and increase low grayscale pixels, grayscale compensation can be performed on all grayscale values ​​in the abnormal region of the image, such as reducing the grayscale values ​​of the sub-pixels with higher grayscale values ​​in the abnormal region of the image while increasing the grayscale values ​​of the sub-pixels with lower grayscale values ​​in the abnormal region of the image.

[0068] The specific value of grayscale compensation is not limited. It can be a pre-set fixed value, which can be configured based on the experience of relevant personnel. The specific value of grayscale compensation can also be determined according to the actual situation of abnormal areas in the image. This embodiment will not elaborate further on this.

[0069] S104. Display the image to be displayed based on the updated grayscale values ​​of each sub-pixel of each pixel.

[0070] After obtaining the updated grayscale values ​​of each sub-pixel of each pixel through the above steps, the image to be displayed can be displayed to complete the processing of the image pixel data of the image to be displayed and realize the normal display of the image to be displayed.

[0071] This embodiment provides a method for processing image pixel data. The method acquires pixel data for each pixel in an image to be displayed, including the grayscale values ​​of each sub-pixel of the pixel. Based on the grayscale values ​​of each sub-pixel of each pixel, anomaly identification is performed on the image to be displayed to obtain abnormal regions. Grayscale compensation is applied to at least a portion of the grayscale values ​​in the abnormal regions according to a target compensation method to obtain updated grayscale values ​​for each sub-pixel of each pixel. The target compensation method includes reducing high-grayscale pixels and / or increasing low-grayscale pixels. Based on the updated grayscale values ​​of each sub-pixel of each pixel, the image to be displayed is shown. Using this method, by applying grayscale compensation to at least a portion of the grayscale values ​​in the identified abnormal regions according to a target compensation method, the problem of abnormal display in abnormal regions of the image can be effectively solved, improving the applicability of image processing.

[0072] Figure 3aThis is a flowchart illustrating an image pixel data processing method according to another embodiment of this application. In this embodiment, based on the grayscale values ​​of each sub-pixel of each pixel, anomaly identification is performed on the image to be displayed to obtain the image anomaly region. Further optimization is achieved by: identifying whether there is an image region in the image to be displayed that meets preset grayscale conditions based on the grayscale values ​​of each sub-pixel of each pixel. The preset grayscale conditions include: the grayscale difference between any two sub-pixels within the image region is greater than a preset difference threshold, and the area of ​​the image region is greater than a preset area threshold. If an image region meeting the preset grayscale conditions exists in the image to be displayed, then the image anomaly region of the image to be displayed is determined based on the image region. Figure 3a As shown, the method includes:

[0073] S201. Obtain the pixel data of each pixel in the image to be displayed. The pixel data includes the grayscale value of each sub-pixel of the pixel.

[0074] S202. Based on the grayscale values ​​of each sub-pixel of each pixel, identify whether there is an image region in the image to be displayed that meets the preset grayscale conditions. The preset grayscale conditions include: the grayscale difference between any two sub-pixels in the image region is greater than the preset difference threshold, and the area of ​​the image region is greater than the preset area threshold.

[0075] In this embodiment, the preset grayscale condition can be understood as the sub-pixels in a large area consisting of only two grayscale values. For example, the preset grayscale condition may include: the grayscale difference between any two sub-pixels in the image area is greater than a preset difference threshold, and the area of ​​the image area is greater than a preset area threshold. The preset difference threshold may refer to the maximum critical value of the grayscale difference, and the preset area threshold may refer to the maximum critical value of the area. The specific values ​​of the preset difference threshold and the preset area threshold can be set based on actual needs.

[0076] In this context, if the grayscale difference between any two sub-pixels within an image region is greater than a preset difference threshold, it can be considered that each sub-pixel within the image region consists of two grayscale values, and the difference between these two grayscale values ​​is greater than the preset difference threshold. For example, each sub-pixel within the image region can be composed of grayscale values ​​of 255 and 0. Alternatively, each sub-pixel within the image region can be composed of two polarized grayscale values, where a value greater than the first grayscale value is considered one type of grayscale, and a value less than the second grayscale value is considered another type of grayscale. The difference between the first and second grayscale values ​​must be greater than the preset difference threshold. For instance, the first and second grayscale values ​​can be set to 20 and 230, respectively. If the grayscale values ​​of sub-pixels in a portion of the image region are all less than 20, and the grayscale values ​​of sub-pixels in the remaining region are all greater than 230, then the grayscale difference between any two sub-pixels within the image region can be considered greater than the preset difference threshold.

[0077] In a specific embodiment, the existence of an image region that meets the preset grayscale conditions in the image to be displayed can be identified based on the grayscale values ​​of each sub-pixel of each pixel, and the identification result can be obtained. Different steps can be performed based on different identification results. This embodiment does not limit the specific process of identifying whether there is an image region that meets the preset grayscale conditions in the image to be displayed. For example, the image region that meets the preset grayscale conditions can be identified sequentially from the upper left corner to the lower right corner of the image to be displayed in a preset order, or it can be directly identified by a preset identification model.

[0078] S203. If there is an image region in the image to be displayed that meets the preset grayscale conditions, then the abnormal image region of the image to be displayed is determined based on the image region.

[0079] After identifying whether there are image regions in the image to be displayed that meet the preset grayscale conditions, if no image regions that meet the preset grayscale conditions are identified, it means that there are no suspected abnormal regions in the image to be displayed, and the image to be displayed can be processed directly.

[0080] After identifying whether there are image regions in the image to be displayed that meet the preset grayscale conditions, if an image region that meets the preset grayscale conditions is identified, the abnormal image region of the image to be displayed can be determined based on the identified image region. For example, the identified image region can be directly determined as the abnormal image region of the image to be displayed, or the abnormal image region of the image to be displayed can be determined by further detection of the identified image region. The means of further detection can be determined based on actual needs.

[0081] In some embodiments, determining abnormal regions of the image to be displayed based on image regions includes:

[0082] Polarity symmetry detection is performed on each row of pixels in the image region to obtain the polarity detection result of each row of pixels. The polarity detection result is used to indicate whether the polarity of each sub-pixel in each row is symmetrical.

[0083] When the polarity detection result indicates that the sub-pixels of the pixels in the target row are asymmetrical, the area where the target row is located is determined as the image abnormal area of ​​the image to be displayed, where the target row is one or more rows in each row of pixels.

[0084] The polarity detection result can be used to indicate whether the polarity of each sub-pixel in each row of pixels is symmetrical; the target row number can be one or more rows in each row of pixels, which can be determined by the polarity detection result of each row of pixels. For example, if the polarity detection result of the first row of pixels indicates that the polarity of each sub-pixel in the row is asymmetrical, then the first row can be considered as the target row number; if the polarity detection result of the first row of pixels indicates that the polarity of each sub-pixel in the row is symmetrical, then the first row can be considered as not the target row number.

[0085] In a specific implementation, the process of determining the image abnormality region of the image to be displayed based on the image region may include, for example, performing polarity symmetry detection on each row of pixels in the image region to obtain the polarity detection result of each row of pixels. Based on the different polarity detection results of each row of pixels, the image abnormality region of the image to be displayed can be determined. For example, if the polarity detection result indicates that the sub-pixels of the pixels in the target row are asymmetrical, the region where the target row is located can be determined as the image abnormality region of the image to be displayed. The specific method of polarity symmetry detection is not limited here, as long as polarity symmetry detection of each row of pixels can be achieved. Based on this, by performing polarity symmetry detection on each row of pixels in the image region and determining the image abnormality region of the image to be displayed based on the polarity symmetry detection result of each row of pixels, the accuracy of the image abnormality region can be further guaranteed.

[0086] In some embodiments, the pixel data further includes the pixel polarity of each sub-pixel of each pixel, and polarity symmetry detection is performed on each row of pixels in the image region to obtain the polarity detection result of each row of pixels, including:

[0087] Based on the pixel polarity and grayscale value of each sub-pixel in each row, calculate the cumulative polarity value corresponding to each row of pixels.

[0088] Detect whether the cumulative polarity value is greater than the polarity threshold;

[0089] If the detected polarity accumulation value is greater than the polarity threshold, then the polarity detection result of each row of pixels is determined to be polarity asymmetry between the sub-pixels of each row of pixels;

[0090] If the detected polarity cumulative value is not greater than the polarity threshold, then the polarity detection result of each row of pixels is determined to be that the polarity is symmetrical between the sub-pixels of each row of pixels.

[0091] Pixel data also includes the pixel polarity of each sub-pixel of each pixel. Pixel polarity is used to characterize the voltage level of the sub-pixel relative to the common electrode VCOM. Pixel polarity is either positive or negative. For example, positive polarity indicates that the voltage of the sub-pixel is higher than that of VCOM, and negative polarity indicates that the voltage of the sub-pixel is lower than that of VCOM.

[0092] In this embodiment, sub-pixels corresponding to different pixel polarities can correspond to different count values. The cumulative polarity value can be considered as the absolute value of the sum of the products of the grayscale value and the count value of each sub-pixel. Taking the grayscale value of each sub-pixel in the image area as composed of 0 and 255 as an example, the count value corresponding to the + 255 grayscale can be 1, the count value corresponding to the - 255 grayscale can be -1, and the count value of the 0 grayscale is 0 regardless of the + or - polarity. When there are 10 + 255 grayscale values, 5 - 255 grayscale values, and 6 0 grayscale values ​​in a certain row, the cumulative polarity value of this row is 10*1+5*(-1)+6*0=5.

[0093] The polarity threshold can be the maximum critical value of the polarity accumulation value. For example, it can refer to how many points in a row of pixels with asymmetric positive and negative values ​​will cause horizontal crosstalk anomalies. It can be set based on the empirical value of how many points will cause horizontal crosstalk.

[0094] In a specific implementation, the polarity detection result of each row of pixels can be determined by calculating the cumulative polarity value corresponding to each row of pixels. For example, it can be detected whether the cumulative polarity value corresponding to each row of pixels is greater than a polarity threshold, and different polarity detection results can be determined for each row of pixels based on different detection results. For instance, if the cumulative polarity value is detected to be greater than the polarity threshold, it means that the cumulative polarity value corresponding to the current row of pixels has exceeded the threshold value that positive and negative asymmetry can cause horizontal crosstalk, and then the polarity detection result of the current row of pixels can be determined to be polarity asymmetry between sub-pixels. If the cumulative polarity value is detected to be less than the polarity threshold, it means that the cumulative polarity value corresponding to the current row of pixels has not yet exceeded the threshold value that positive and negative asymmetry can cause horizontal crosstalk, and then the polarity detection result of the current row of pixels can be determined to be polarity symmetry between sub-pixels. Based on this, by detecting whether the cumulative polarity value corresponding to each row of pixels is greater than the polarity threshold, the polarity detection result of each row of pixels is determined, providing a feasible detection method for polarity symmetry detection in image regions and improving the accuracy of polarity detection results.

[0095] S204. Perform grayscale compensation on at least some grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method includes: reducing high grayscale pixels and / or increasing low grayscale pixels.

[0096] S205. Display the image to be displayed based on the updated grayscale values ​​of each sub-pixel of each pixel.

[0097] This application provides an image pixel data processing method that identifies whether there are image regions in the image to be displayed that meet preset grayscale conditions based on the grayscale values ​​of each sub-pixel of each pixel. This method can accurately identify abnormal image regions in the image to be displayed, providing a basis for subsequent grayscale compensation.

[0098] In some embodiments, before performing grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel, the method further includes:

[0099] Determine the maximum polarity summation value among the polarity summation values ​​corresponding to each row of pixels;

[0100] Based on the maximum cumulative polarity value, the target compensation method, and the preset compensation table, the target grayscale compensation value corresponding to the abnormal area of ​​the image is determined. The preset compensation table establishes at least one compensation method corresponding to different preset polarity values ​​and grayscale compensation values ​​corresponding to at least one compensation method. The preset polarity value is calculated based on the polarity threshold and the display resolution.

[0101] The preset compensation table can be a pre-configured compensation table used to determine the target grayscale compensation value corresponding to the abnormal area of ​​the image. The target grayscale compensation value is the number of grayscale compensations to be performed on the abnormal area of ​​the image. The preset compensation table can establish at least one compensation method corresponding to different preset polarity values ​​and at least one grayscale compensation value corresponding to the compensation method. The specific value of the preset polarity value can be calculated based on the polarity threshold and the display resolution.

[0102] Table 2 is a preset compensation table provided in this embodiment. For example, the compensation method (i.e. the target compensation method) can be determined and the preset compensation table can be configured before the display product is shipped.

[0103] Table 2 Preset Compensation Table

[0104]

[0105]

[0106] Where Y can be the polarity threshold, and H can be the display resolution, such as the resolution of each row of the display. For example, H can be the Ultra High Definition (UHD) resolution, i.e., 3840. 1.5H can be because there are 1.5H (H*3 (RGB three types of sub-pixels) / 2 (+- two types of polarities)) of a certain polarity in a row of elements. Then the maximum difference between the two polarities is 1.5H (when the other polarity is all 0 grayscale). The 10 in the first row of the preset compensation table can indicate that the different polarity differences in a row of elements are divided into 10 segments for debugging. In this embodiment, it can also be divided into other numbers of segments, such as 20, which can be determined according to the memory size of the Timing Controller (TCON).

[0107] The specific values ​​for a1-a10, b1-b10, and c1-c10 in the preset compensation table can be obtained through actual debugging. The preset compensation table defines three compensation methods, and in practical applications, the specific method to use can be selected according to display needs (e.g., M+N, M, or N). Selection criteria can include improving horizontal crosstalk and avoiding other display problems, such as obvious display distortion. M+N represents the number of data points required to compensate for both low and high gray levels (M=N). M is the gray level value needed to reduce high gray levels alone, and N is the compensation value needed to increase low gray levels alone. Generally, the values ​​of M and N are within 16, with the last column having the largest compensation data and the first column having the smallest.

[0108] In a specific implementation, the specific value of the target grayscale compensation can be determined based on the cumulative polarity value of each row of pixels. For example, the maximum cumulative polarity value among the cumulative polarity values ​​corresponding to each row of pixels can be determined first. Then, based on the determined maximum cumulative polarity value, the target compensation method, and a preset compensation table, the target grayscale compensation value corresponding to the abnormal image area can be determined. For instance, by looking up the preset compensation table, it can be determined which interval of different preset polarity values ​​the maximum cumulative polarity value falls into, thus determining the specific target grayscale compensation value based on the target compensation method. On this basis, by accurately determining the target grayscale compensation value corresponding to the abnormal image area based on the cumulative polarity value of each row of pixels and the preset compensation table, a data foundation is provided for subsequent accurate grayscale compensation of the abnormal image area, further effectively solving the problem of abnormal display in abnormal image areas.

[0109] In some embodiments, grayscale compensation is performed on at least a portion of the grayscale values ​​in the abnormal region of the image according to a target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel, including:

[0110] When the target compensation method is to reduce the gray level of high gray level pixels, the gray level value of the first sub-pixel in the abnormal region of the image is reduced based on the target gray level compensation value to obtain the updated gray level value of each sub-pixel of each pixel. The first sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is greater than the first gray level threshold.

[0111] When the target compensation method is to enhance low grayscale pixels, the grayscale value of the second sub-pixel in the abnormal region of the image is enhanced based on the target grayscale compensation value to obtain the updated grayscale value of each sub-pixel of each pixel. The second sub-pixel is the sub-pixel in the abnormal region of the image whose grayscale value is less than the second grayscale threshold.

[0112] The first sub-pixel can be a sub-pixel in the image anomaly region whose grayscale value is greater than a first grayscale threshold, and the second sub-pixel can be a sub-pixel in the image anomaly region whose grayscale value is less than a second grayscale threshold. The first and second grayscale thresholds can be set by relevant personnel; for example, the difference between the first and second grayscale thresholds can be a preset difference threshold.

[0113] Specifically, when the target compensation method is to reduce high-grayscale pixels, the grayscale value of the first sub-pixel in the abnormal image region can be reduced based on the determined target grayscale compensation value to obtain the updated grayscale values ​​of each sub-pixel of each pixel. When the target compensation method is to increase low-grayscale pixels, the grayscale value of the second sub-pixel in the abnormal image region can be increased based on the determined target grayscale compensation value to obtain the updated grayscale values ​​of each sub-pixel of each pixel. Based on this, corresponding compensation methods are provided for the actual conditions of different abnormal image regions, achieving accurate processing of image pixel data in the image to be displayed, thereby ensuring the normal display of the image.

[0114] In some embodiments, the target grayscale compensation value includes a first target grayscale compensation value and a second target grayscale compensation value. Grayscale compensation is performed on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel, including:

[0115] When the target compensation method is to reduce the gray level of high gray level pixels and increase the gray level of low gray level pixels, the gray level value of the first sub-pixel in the abnormal region of the image is reduced based on the first target gray level compensation value, and the gray level value of the second sub-pixel in the abnormal region of the image is increased based on the second target gray level compensation value, so as to obtain the updated gray level value of each sub-pixel of each pixel. The first sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is greater than the first gray level threshold, and the second sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is less than the second gray level threshold.

[0116] When it is necessary to compensate for both high-grayscale pixels and low-grayscale pixels in abnormal areas of an image, the target grayscale compensation value may include a first target grayscale compensation value and a second target grayscale compensation value. The first target grayscale compensation value may refer to the number of grayscale values ​​of the first sub-pixel that are compensated, and the second target grayscale compensation value may refer to the number of grayscale values ​​of the second sub-pixel that are compensated.

[0117] Specifically, when the target compensation method is to reduce high-grayscale pixels and increase low-grayscale pixels, the difference between the grayscale value of the first sub-pixel and the first target grayscale compensation value can be used as the updated grayscale value of the first sub-pixel in the image abnormal region, and the difference between the grayscale value of the second sub-pixel and the second target grayscale compensation value can be used as the updated grayscale value of the second sub-pixel in the image abnormal region. Based on this, corresponding compensation methods are provided for the actual conditions of different image abnormal regions, achieving accurate processing of image pixel data in the image to be displayed, thereby ensuring the normal display of the image.

[0118] Figure 3b This is a schematic flowchart illustrating another image pixel data processing method provided in an embodiment of this application, as shown below. Figure 3b As shown, the System-on-a-Chip (SOC) can transmit pixel data to the timing controller TCON, which first identifies whether the "abnormal area" is a large area of ​​"string" and determines the size of the "abnormal area" (e.g., how many rows it mainly consists of). During the identification process, large areas are identified based on having only two gray levels. For example, when a large area of ​​pixels in the image displays only two gray levels, it is determined to be a large area of ​​abnormal string (i.e., based on the gray level values ​​of each sub-pixel of each pixel, it identifies whether there is an image area in the image to be displayed that meets the preset gray level conditions. The preset gray level conditions include: the gray level difference between any two sub-pixels in the image area is greater than the preset difference threshold, and the area of ​​the image area is greater than the preset area threshold).

[0119] Understandably, once it is determined that the "abnormal area" contains a large area of ​​"strings", it only identifies the abnormal image. However, this abnormal image may not necessarily have horizontal crosstalk issues. Therefore, it is possible to further determine whether the positive and negative polarities of the "abnormal area" are asymmetrical.

[0120] Figure 3c This is a schematic diagram of a screen showing a string displayed normally, provided in an embodiment of this application. Figure 3c As shown, the "abnormal area" contains a large area of ​​"H string", but there is no horizontal crosstalk problem in this screen, that is, the "H string" is displayed normally.

[0121] Figure 3d This is a schematic diagram of a screen showing an abnormal string display according to an embodiment of this application, such as... Figure 3d As shown, the "abnormal area" contains a large area of ​​"M string", but horizontal crosstalk occurs on this screen, that is, the "M string" is displayed abnormally.

[0122] For example, the process of determining whether the positive and negative polarities are asymmetrical can be as follows: Within the "abnormal region," calculate the absolute value of the sum of counts within an entire row (denoted as X); if X ≥ Y, it proves that the positive and negative polarities of this row are asymmetrical, and the next step of data compensation can be performed (i.e., based on the pixel polarity and grayscale value of each sub-pixel of each row, calculate the cumulative polarity value corresponding to each row of pixels; if the cumulative polarity value is detected to be greater than the polarity threshold, calculate the number of pixel crosstalks corresponding to each row of pixels; if the polarity detection result indicates that the polarity is asymmetrical among the sub-pixels of the pixels in the target row, determine the area where the target row is located as the image abnormal region of the image to be displayed, where the target row is one or more rows in each row of pixels); at the same time, record the maximum value Y of X in all rows of the "abnormal region." max (That is, determine the maximum polarity summation value among the polarity summation values ​​corresponding to each row of pixels).

[0123] Finally, you can look up the data in the table and adjust the gray levels accordingly. Let's say Y... max If the value falls between Y+(1.5HY)*(8 / 10) and Y+(1.5HY)*(9 / 10) in Table 2, and the target compensation method has been pre-selected as reducing high grayscale pixels (i.e. using M compensation method), then the high grayscale value in the "abnormal area" can be reduced by b9 (i.e., the target grayscale compensation value corresponding to the abnormal area of ​​the image is determined according to the maximum polarity accumulation value, the target compensation method and the preset compensation table).

[0124] Corresponding to the image pixel data processing method in the above embodiments, Figure 4 This is a structural block diagram of an image pixel data processing device provided in one embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0125] Reference Figure 4 The device includes:

[0126] The acquisition module 301 is used to acquire pixel data of each pixel in the image to be displayed. The pixel data includes the grayscale values ​​of each sub-pixel of the pixel.

[0127] The anomaly detection module 302 is used to perform anomaly detection on the image to be displayed based on the grayscale values ​​of each sub-pixel of each pixel, and obtain the abnormal regions of the image to be displayed.

[0128] The grayscale compensation module 303 is used to perform grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method, so as to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method includes: reducing high grayscale pixels and / or increasing low grayscale pixels.

[0129] Display module 304 is used to display the image to be displayed based on the updated grayscale values ​​of each sub-pixel of each pixel.

[0130] This embodiment provides an image pixel data processing apparatus. The apparatus acquires pixel data for each pixel in an image to be displayed via an acquisition module. The pixel data includes the grayscale values ​​of each sub-pixel of the pixel. An anomaly identification module identifies anomalies in the image based on the grayscale values ​​of each sub-pixel of each pixel, obtaining abnormal regions in the image. A grayscale compensation module performs grayscale compensation on at least a portion of the grayscale values ​​in the abnormal regions according to a target compensation method, resulting in updated grayscale values ​​for each sub-pixel of each pixel. The target compensation method includes reducing high-grayscale pixels and / or increasing low-grayscale pixels. A display module displays the image based on the updated grayscale values ​​of each sub-pixel of each pixel. Using this apparatus, by performing grayscale compensation on at least a portion of the identified abnormal regions according to a target compensation method, the problem of abnormal display in abnormal regions of the image can be effectively solved, improving the applicability of image processing.

[0131] Optionally, the anomaly detection module includes:

[0132] The recognition unit is used to identify whether there is an image region in the image to be displayed that meets the preset gray level conditions based on the gray level values ​​of each sub-pixel of each pixel. The preset gray level conditions include: the gray level difference between any two sub-pixels in the image region is greater than a preset difference threshold, and the area of ​​the image region is greater than a preset area threshold.

[0133] The determining unit is used to determine the abnormal image region of the image to be displayed based on the image region if there is an image region in the image to be displayed that meets the preset grayscale conditions.

[0134] Optionally, the determined unit includes:

[0135] The symmetry detection subunit is used to perform polarity symmetry detection on each row of pixels in the image region to obtain the polarity detection result of each row of pixels. The polarity detection result is used to indicate whether the polarity of each sub-pixel in each row of pixels is symmetrical.

[0136] If the polarity of sub-pixels in the target row number is asymmetrical as indicated by the polarity detection result, the region where the target row number is located is identified as an image abnormality region of the image to be displayed, wherein the target row number is one or more rows in each row of pixels.

[0137] Optionally, the symmetric detection subunit is specifically used for:

[0138] Based on the pixel polarity and grayscale value of each sub-pixel in each row, calculate the cumulative polarity value corresponding to each row of pixels.

[0139] Detect whether the cumulative polarity value is greater than the polarity threshold;

[0140] If the detected polarity accumulation value is greater than the polarity threshold, then the polarity detection result of each row of pixels is determined to be polarity asymmetry between the sub-pixels of each row of pixels;

[0141] If the detected polarity cumulative value is not greater than the polarity threshold, then the polarity detection result of each row of pixels is determined to be that the polarity is symmetrical between the sub-pixels of each row of pixels.

[0142] Optionally, the image pixel data processing apparatus provided in this embodiment further includes:

[0143] The first determining module is used to determine the maximum polarity accumulation value between the polarity accumulation values ​​corresponding to each row of pixels before performing grayscale compensation on at least some grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel.

[0144] The second determining module is used to determine the target grayscale compensation value corresponding to the abnormal region of the image before performing grayscale compensation on at least some grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method and the preset compensation table are established with at least one compensation method corresponding to different preset polarity values ​​and grayscale compensation values ​​corresponding to at least one compensation method. The preset polarity values ​​are calculated based on the polarity threshold and the display resolution.

[0145] Optionally, the grayscale compensation module is specifically used for:

[0146] When the target compensation method is to reduce the gray level of high gray level pixels, the gray level value of the first sub-pixel in the abnormal region of the image is reduced based on the target gray level compensation value to obtain the updated gray level value of each sub-pixel of each pixel. The first sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is greater than the first gray level threshold.

[0147] When the target compensation method is to enhance low grayscale pixels, the grayscale value of the second sub-pixel in the abnormal region of the image is enhanced based on the target grayscale compensation value to obtain the updated grayscale value of each sub-pixel of each pixel. The second sub-pixel is the sub-pixel in the abnormal region of the image whose grayscale value is less than the second grayscale threshold.

[0148] Optionally, the target grayscale compensation value includes a first target grayscale compensation value and a second target grayscale compensation value. The grayscale compensation module is specifically used for:

[0149] When the target compensation method is to reduce the gray level of high gray level pixels and increase the gray level of low gray level pixels, the gray level value of the first sub-pixel in the abnormal region of the image is reduced based on the first target gray level compensation value, and the gray level value of the second sub-pixel in the abnormal region of the image is increased based on the second target gray level compensation value, so as to obtain the updated gray level value of each sub-pixel of each pixel. The first sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is greater than the first gray level threshold, and the second sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is less than the second gray level threshold.

[0150] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0151] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0152] This application also provides a display. Figure 5 This is a schematic diagram of the structure of a display provided in one embodiment of this application, as shown below. Figure 5 As shown, the display includes: at least one processor 401, a memory 402, an input device 403, an output device 404, and a computer program stored in the memory 402 and executable on at least one processor 401. When the processor 401 executes the computer program, it implements the steps in any of the above-described method embodiments.

[0153] Input device 403 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the display. Output device 404 may include display devices such as a display screen.

[0154] This application also provides a computer-readable storage medium storing a computer program, which, when executed by processor 401, implements the steps in the above-described method embodiments.

[0155] This application provides a computer program product that, when run on a display, enables the display to perform the steps described in the various method embodiments above.

[0156] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 401, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / display, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0157] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0158] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0159] In the embodiments provided in this application, it should be understood that the disclosed apparatus / display and method can be implemented in other ways. For example, the apparatus / display embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0160] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0161] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for processing image pixel data, characterized in that, include: Obtain pixel data for each pixel in the image to be displayed, wherein the pixel data includes the grayscale value of each sub-pixel of the pixel; Based on the grayscale values ​​of each sub-pixel of each pixel and a preset recognition model, anomaly recognition is performed on the image to be displayed to obtain the image anomaly region of the image to be displayed; wherein, the preset recognition model is a model obtained by training a neural network model in advance based on the grayscale values ​​of each sample and the sample anomaly region of multiple sample images. According to the target compensation method, at least some grayscale values ​​in the abnormal region of the image are compensated for grayscale values ​​to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method includes: reducing high grayscale pixels and / or increasing low grayscale pixels. The image to be displayed is shown based on the updated grayscale values ​​of each sub-pixel of each pixel; The pixel data further includes the pixel polarity of each sub-pixel of the pixel. Before performing grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel, the method further includes: Based on the pixel polarity and grayscale value of each sub-pixel of each row of pixels in the abnormal region of the image, calculate the cumulative polarity value corresponding to each row of pixels; Determine the maximum polarity summation value among the polarity summation values ​​corresponding to the pixels in each row; Based on the maximum polarity accumulation value, the target compensation method, and the preset compensation table, the target grayscale compensation value corresponding to the abnormal image region is determined. The target grayscale compensation value is the number of grayscale compensations to be performed on the abnormal image region. It is used to comprehensively perform grayscale compensation on at least some grayscale values ​​in the abnormal image region by combining the target compensation method. The preset compensation table establishes at least one compensation method corresponding to different preset polarity values ​​and grayscale compensation values ​​corresponding to the at least one compensation method. The preset polarity value is calculated based on the polarity threshold and the display resolution.

2. The image pixel data processing method as described in claim 1, characterized in that, The step of identifying anomalies in the image to be displayed based on the grayscale values ​​of each sub-pixel of each pixel to obtain the abnormal regions of the image to be displayed includes: Based on the grayscale values ​​of each sub-pixel of each pixel, identify whether there is an image region in the image to be displayed that meets the preset grayscale conditions. The preset grayscale conditions include: the grayscale difference between any two sub-pixels in the image region is greater than a preset difference threshold, and the area of ​​the image region is greater than a preset area threshold. If there is an image region in the image to be displayed that meets the preset grayscale conditions, then the abnormal image region of the image to be displayed is determined based on the image region.

3. The image pixel data processing method as described in claim 2, characterized in that, The step of determining the abnormal image region of the image to be displayed based on the image region includes: Polarity symmetry detection is performed on each row of pixels in the image region to obtain the polarity detection result of each row of pixels. The polarity detection result is used to indicate whether the polarity of each sub-pixel of each row of pixels is symmetrical. When the polarity detection result indicates that the polarity of each sub-pixel in the target row is asymmetrical, the area where the target row is located is determined as the image abnormal area of ​​the image to be displayed, wherein the target row is one or more rows in each row of pixels.

4. The image pixel data processing method as described in claim 3, characterized in that, The pixel data also includes the pixel polarity of each sub-pixel of each pixel. The step of performing polarity symmetry detection on each row of pixels in the image region to obtain the polarity detection result for each row of pixels includes: Based on the pixel polarity and grayscale value of each sub-pixel of each row of pixels, calculate the cumulative polarity value corresponding to each row of pixels; Detect whether the accumulated polarity value is greater than the polarity threshold; If the detected polarity accumulation value is greater than the polarity threshold, then the polarity detection result of each row of pixels is determined to be polarity asymmetry between the sub-pixels of each row of pixels; If the detected polarity accumulation value is not greater than the polarity threshold, then the polarity detection result of each row of pixels is determined to be that the polarity is symmetrical between the sub-pixels of each row of pixels.

5. The image pixel data processing method as described in claim 1, characterized in that, The step of performing grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel includes: When the target compensation method is to reduce high grayscale pixels, the grayscale value of the first sub-pixel in the abnormal region of the image is reduced based on the target grayscale compensation value to obtain the updated grayscale value of each sub-pixel of each pixel. The first sub-pixel is the sub-pixel in the abnormal region of the image whose grayscale value is greater than the first grayscale threshold. When the target compensation method is to enhance low grayscale pixels, the grayscale value of the second sub-pixel in the abnormal region of the image is enhanced based on the target grayscale compensation value to obtain the updated grayscale value of each sub-pixel of each pixel. The second sub-pixel is the sub-pixel in the abnormal region of the image whose grayscale value is less than the second grayscale threshold.

6. The image pixel data processing method as described in claim 1, characterized in that, The target grayscale compensation value includes a first target grayscale compensation value and a second target grayscale compensation value. The step of performing grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel includes: When the target compensation method is to reduce the gray level of high gray level pixels and increase the gray level of low gray level pixels, the gray level value of the first sub-pixel in the abnormal region of the image is reduced based on the first target gray level compensation value, and the gray level value of the second sub-pixel in the abnormal region of the image is increased based on the second target gray level compensation value, so as to obtain the updated gray level value of each sub-pixel of each pixel. The first sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is greater than the first gray level threshold, and the second sub-pixel is the sub-pixel in the abnormal region of the image whose gray level value is less than the second gray level threshold.

7. An image pixel data processing apparatus, characterized in that, include: The acquisition module is used to acquire pixel data of each pixel in the image to be displayed, wherein the pixel data includes grayscale values ​​of each sub-pixel of the pixel; An anomaly detection module is used to perform anomaly detection on the image to be displayed based on the grayscale values ​​of each sub-pixel of each pixel and a preset detection model to obtain the image anomaly region of the image to be displayed. The preset detection model is a model obtained by training a neural network model based on the grayscale values ​​of each sample and the sample anomaly region of multiple sample images. A grayscale compensation module is used to perform grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to a target compensation method, so as to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The target compensation method includes: reducing high grayscale pixels and / or increasing low grayscale pixels. The display module is used to display the image to be displayed based on the updated grayscale values ​​of each sub-pixel of each pixel; The pixel data further includes the pixel polarity of each sub-pixel of the pixel, and the processing device further includes: Before performing grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel, the polarity accumulation value corresponding to each row of pixels is calculated based on the pixel polarity and grayscale value of each sub-pixel of each row of pixels in the abnormal region of the image. The first determining module is used to determine the maximum polarity accumulation value between the polarity accumulation values ​​corresponding to each row of pixels before performing grayscale compensation on at least a portion of the grayscale values ​​in the abnormal region of the image according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel. The second determining module is used to determine the target grayscale compensation value corresponding to the image abnormal region before performing grayscale compensation on at least a portion of the grayscale values ​​in the image abnormal region according to the target compensation method to obtain the updated grayscale values ​​of each sub-pixel of each pixel. This is done based on the maximum polarity accumulation value, the target compensation method, and a preset compensation table. The target grayscale compensation value is the number of grayscale compensations to be performed on the image abnormal region. The module is used to perform grayscale compensation on at least a portion of the grayscale values ​​in the image abnormal region by comprehensively considering the target compensation method. The preset compensation table establishes at least one compensation method corresponding to different preset polarity values ​​and the grayscale compensation value corresponding to the at least one compensation method. The preset polarity value is calculated based on a polarity threshold and the display resolution.

8. A display comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the image pixel data processing method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the image pixel data processing method as described in any one of claims 1 to 6.

Images