Image processing method and device, electronic equipment and storage medium

Abstract

An image processing method comprises: (S12) acquiring an original image and converting first color data of a pixel point in the original image into second color data corresponding to a set color space; (S14) calculating a feature weighting value corresponding to the pixel point according to the second color data and a preset rule; and (S16) judging uniformity of the pixel point according to the feature weighting value to mark the pixel point. The application further discloses an image processing device, an electronic device and a storage medium.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of image processing, and in particular to an image processing method, an image processing device, an electronic device, and a storage medium. BACKGROUND

[0002] In related technologies, a smaller color gamut in which all pixels can be displayed is found, and all pixels are corrected to be displayed in the color gamut. Although this method can obtain a better uniformity effect, the color gamut is greatly lost, resulting in a low color saturation of a displayed image and an inability to truly restore colors. SUMMARY

[0003] Therefore, the present application provides an image processing method, an image processing device, an electronic device, and a storage medium.

[0004] The image processing method of the present application embodiment includes:

[0005] obtaining an original image and converting first color data of a pixel point in the original image into second color data corresponding to a set color space;

[0006] calculating a feature weighting value corresponding to the pixel point according to the second color data and a preset rule; and

[0007] judging uniformity of the pixel point according to the feature weighting value to mark the pixel point.

[0008] In some embodiments, the set color space is an HSV color space.

[0009] In some embodiments, the second color data includes hue data, saturation data, and brightness data, and the calculating of the feature weighting value corresponding to the pixel point according to the second color data and the preset rule includes:

[0010] preprocessing the second color data to obtain third color data;

[0011] quantitatively processing the third color data according to a color block rule to obtain quantized values corresponding to the hue data, the saturation data, and the brightness data; and

[0012] determining the feature weighting value according to the quantized values corresponding to the hue data, the saturation data, and the brightness data.

[0013] In some embodiments, the preprocessing of the second color data to obtain third color data includes:

[0014] determining the second color data with a brightness data less than a first preset value as black data; and

[0015] determining the second color data with a brightness data greater than a second preset value as white data; andThe second color data whose saturation data is less than a second preset value and whose brightness data is greater than a third preset value is determined as white color data.

[0016] In some embodiments, the judging the uniformity of the pixel point according to the feature weighting value to mark the pixel point comprises:

[0017] determining a feature difference between the feature weighting value of the current pixel point and the feature weighting value of the adjacent pixel point;

[0018] when the feature difference is greater than a first threshold, marking the current pixel point as a first type of boundary pixel point;

[0019] when the feature difference is not greater than the first threshold, marking the current pixel point as a non-boundary pixel point.

[0020] In some embodiments, the judging the uniformity of the pixel point according to the feature weighting value to mark the pixel point further comprises:

[0021] determining a neighborhood corresponding to the first type of boundary pixel point, and calculating a first type of boundary pixel point number in the neighborhood corresponding to any first type of boundary pixel point;

[0022] when the first type of boundary pixel point number is greater than a second threshold, marking the first type of boundary pixel point as a first type of non-uniform pixel point; and

[0023] when the first type of boundary pixel point number is not greater than the second threshold, marking the first type of boundary pixel point and the non-boundary pixel point as uniform pixel points;

[0024] generating a first uniformity distribution image according to the first type of non-uniform pixel point and the uniform pixel point.

[0025] In some embodiments, the judging the uniformity of the pixel point according to the feature weighting value to mark the pixel point further comprises:

[0026] performing dilation processing on the uniformity distribution image to obtain a second type of non-uniform pixel point, and generating a second uniformity distribution image;

[0027] in the second uniformity distribution image, marking a second type of non-uniform pixel whose adjacent pixels in a preset direction are uniform pixels as a second type of boundary pixel point;

[0028] In the row and / or column where the target boundary pixel is located, the second type of boundary pixel and a preset number of pixels adjacent to the second type of boundary pixel are marked as transition pixels, a second type of non-uniform pixel that is not the transition pixel is marked as a target non-uniform pixel, and a uniform pixel that is not the transition pixel and is not the second type of non-uniform pixel is marked as a target uniform pixel.

[0029] In some embodiments, marking the second type of boundary pixel and the preset number of pixels adjacent to the second type of boundary pixel as transition pixels comprises:

[0030] Randomly selecting a value within a preset range to obtain the preset number.

[0031] In some embodiments, the image processing method further comprises:

[0032] Determining a uniformity compensation matrix according to the uniformity of the pixels;

[0033] Processing the pixels according to the uniformity compensation matrix to obtain a display image.

[0034] In some embodiments, determining the uniformity compensation matrix according to the uniformity of the pixels comprises:

[0035] Respectively obtaining a first color gamut conversion matrix corresponding to the target non-uniform pixel, a second color gamut conversion matrix corresponding to the transition pixel, and a third color gamut conversion matrix corresponding to the target uniform pixel, and obtaining a display pixel conversion matrix of a light emitting element on a display panel corresponding to each pixel; and

[0036] The processing the pixels according to the uniformity compensation matrix to obtain a display image comprises:

[0037] Processing the corresponding target non-uniform pixel according to the first color gamut conversion matrix and the display pixel conversion matrix;

[0038] Processing the corresponding transition pixel according to the second color gamut conversion matrix and the display pixel conversion matrix; and

[0039] Processing the corresponding target uniform pixel according to the third color gamut conversion matrix and the display pixel conversion matrix.

[0040] In some embodiments, the first color gamut conversion matrix corresponds to a first color gamut, the second color gamut conversion matrix corresponds to a second color gamut, the third color gamut conversion matrix corresponds to a third color gamut, the range of the first color gamut is greater than the range of the second color gamut, and the range of the second color gamut is greater than the range of the third color gamut.

[0041] In some embodiments, the image processing method comprises:

[0042] determining the third color gamut according to a common color gamut range of all light emitting elements;

[0043] determining the second color gamut according to the third color gamut and a first ratio; and

[0044] determining the first color gamut according to the third color gamut and a second ratio, the second ratio being greater than the first ratio.

[0045] In some embodiments, the separately obtaining the first color gamut conversion matrix corresponding to the target non-uniform pixel point, the second color gamut conversion matrix corresponding to the transition pixel point, and the third color gamut conversion matrix corresponding to the target uniform pixel point comprises:

[0046] determining the first color gamut conversion matrix according to the color coordinates and brightness of the three primary colors in the first color gamut;

[0047] determining the second color gamut conversion matrix according to the color coordinates and brightness of the three primary colors in the second color gamut; and

[0048] determining the third color gamut conversion matrix according to the color coordinates and brightness of the three primary colors in the third color gamut.

[0049] In some embodiments, the obtaining the display pixel conversion matrix of the light emitting element on the display panel corresponding to each pixel point comprises:

[0050] determining the corresponding display pixel matrix according to the RGB color data of the light emitting element on the display panel corresponding to each pixel point.

[0051] The image processing device of the embodiments of the present application comprises:

[0052] an obtaining module, which can be used to obtain an original image and convert first color data of a pixel point in the original image into second color data corresponding to a set color space;

[0053] a calculating module, which can be used to calculate a feature weighting value corresponding to the pixel point according to the second color data and a preset rule; and

[0054] a determining module, which can be used to judge the uniformity of the pixel point according to the feature weighting value to mark the pixel point.

[0055] The embodiments of the present application also provide an electronic device, comprising:

[0056] one or more processors, a memory; and

[0057] One or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, include instructions for performing any of the image processing methods described above.

[0058] The embodiments of the present application also provide a nonvolatile computer readable storage medium of a computer program, which, when executed by one or more processors, causes the processors to perform any of the image processing methods described above.

[0059] In the image processing method, the image processing device, the electronic device and the computer storage medium of the embodiments of the present application, the first color data of the acquired original image is converted into corresponding second color data in a set color space, and the feature weighting value corresponding to the pixel point is calculated according to the second color data and a preset rule, so as to determine the uniformity of the pixel point through the feature weighting value, and the pixel point of the image can be marked according to the uniformity of the pixel point. In this way, it can be decided according to the uniformity of the image content whether to reduce the uniformity of some regions to improve the color gamut, so as to configure different uniformity compensation matrices for each pixel, improve the color saturation of the image on the basis of ensuring the overall uniformity to improve the picture quality, and enhance the user experience.

[0060] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0061] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the appended drawings, wherein:

[0062] Figure 1 is a flowchart of an image processing method of some embodiments of the present application;

[0063] Figure 2 is a module schematic diagram of an image processing device of some embodiments of the present application;

[0064] Figure 3 is a module schematic diagram of an electronic device of some embodiments of the present application;

[0065] Figure 4 is a module schematic diagram of a storage medium connected to a processor of some embodiments of the present application;

[0066] Figure 5 is a schematic diagram of RGB color space and HSV color space of some embodiments of the present application;

[0067] Figure 6 is a flowchart of an image processing method of some embodiments of the present application;

[0068] Figure 7 is a flowchart of an image processing method according to some embodiments of the present application;

[0069] Figure 8 is a scenario diagram of an image processing method according to some embodiments of the present application;

[0070] Figure 9 is a further scenario diagram of an image processing method according to some embodiments of the present application;

[0071] Figure 10 is a flowchart of an image processing method according to some embodiments of the present application;

[0072] Figure 11 is a neighborhood scenario diagram of an image processing method according to some embodiments of the present application;

[0073] Figure 12 is a further scenario diagram of an image processing method according to some embodiments of the present application;

[0074] Figure 13 is a flowchart of an image processing method according to some embodiments of the present application;

[0075] Figure 14 is a further scenario diagram of an image processing method according to some embodiments of the present application;

[0076] Figure 15 is a flowchart of an image processing method according to some embodiments of the present application;

[0077] Figure 16 is a flowchart of an image processing method according to some embodiments of the present application;

[0078] Figure 17 is a flowchart of an image processing method according to some embodiments of the present application;

[0079] Figure 18 is a color gamut diagram of an image processing method according to some embodiments of the present application.

[0080] Main element symbol explanation:

[0081] Image processing apparatus 10, acquisition module 12, calculation module 14, determination module 16, calculation module 18, adjustment module 19, electronic device 100, processor 20, memory 30, program 32, storage medium 40, computer program 42, LED display screen 50. DETAILED DESCRIPTION

[0082] Embodiments of the present application are described below in detail with reference to the accompanying drawings, wherein the same or similar components are denoted by the same or similar reference numerals throughout the drawings. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0083] Generally, an LED display screen is assembled by a plurality of LEDs, and a large number of LEDs have differences in photoelectric parameters. When playing pictures, the assembled whole display screen often has non-uniform phenomena such as mottling, mosaic, and screen. The uniformity of chrominance and brightness of the LEDs is an important factor affecting the viewing effect, and is also the most difficult factor to control, which seriously hinders the development of the LED display screen industry. At present, for the adjustment of chrominance, a bin screening method is usually used to narrow the difference of chrominance of each pixel. However, due to the great difference in brightness and chrominance of LEDs produced by different manufacturers or by the same manufacturer at different times, and the fact that the LEDs required for assembling a large-area LED display screen must belong to the same batch, the cost is greatly increased. In addition, even for the same batch of LEDs, the drift speed of the wavelength center and the brightness decay speed are different, thereby intensifying the non-uniformity of chrominance and brightness of the full-color LED display screen, and bringing greater difficulty to the correction of chrominance and brightness uniformity.

[0084] In the related art, in the LED display technology, one LED is one pixel point, a smaller color gamut in which all the pixel points can display can be found, and all the pixel points in the LED display screen are corrected to the color gamut display of the LED. Although this method can obtain a better uniformity effect, the color gamut is greatly lost, resulting in low color saturation and serious color distortion of the image when the LED display screen displays the image.

[0085] Therefore, in view of the above Figure 1 The present application provides an image processing method, which comprises the following steps:

[0086] S12, acquiring an original image and converting first color data of a pixel point in the original image into second color data corresponding to a set color space;

[0087] S14, calculating a feature weighting value corresponding to the pixel point according to the second color data and a preset rule; and

[0088] S16, judging the uniformity of the pixel point according to the feature weighting value to mark the pixel point.

[0089] Therefore, in view of the above Figure 2 The present application also provides an image processing device 10. The image processing device 10 comprises an acquisition module 12, a calculation module 14, and a determination module 16.

[0090] S12 can be implemented by the acquisition module 12, S14 can be implemented by the calculation module 14, and S16 can be implemented by the determination module 16.

[0091] Alternatively, the acquisition module 12 can be configured to acquire the original image and convert the first color data of the pixel points in the original image into second color data corresponding to a preset color space.

[0092] The calculation module 14 can be configured to calculate the feature weighting value corresponding to the pixel points according to the second color data and a preset rule.

[0093] The determination module 16 can be configured to determine the uniformity of the pixel points according to the feature weighting value to mark the pixel points.

[0094] Referring to Figure 3 The electronic device 100 of the present application further includes one or more processors 20, a memory 30, and one or more programs 32, wherein the one or more programs 32 are stored in the memory 30 and executed by the one or more processors 20. The program 32 includes instructions for executing the image processing method described above by the processor 20.

[0095] Referring to Figure 4 The present application further provides a non-volatile computer-readable storage medium 40, which stores a computer program 42. When the computer program 42 is executed by the one or more processors 20, the processor 20 executes the image processing method described above.

[0096] In the image processing method, the image processing device 10, the electronic device 100, and the storage medium 40 of the present application, the first color data of the acquired original image is converted into corresponding second color data in a preset color space, and the feature weighting value corresponding to the pixel points is calculated according to the second color data and a preset rule, so as to determine the uniformity of the pixel points through the feature weighting value, and mark the pixel points of the image according to the uniformity of the pixel points. In this way, it can be decided according to the image content whether to reduce the uniformity of some regions to improve the color gamut, and different uniformity compensation matrices can be configured for each pixel to improve the color saturation on the basis of ensuring the overall uniformity, thereby improving the picture quality and enhancing the user experience.

[0097] In some embodiments, the electronic device 100 can be a television, a computer, a mobile phone, a tablet or an electronic watch, a smart wearable device such as a VR device, an AR device, etc. The electronic device 100 includes an LED display screen 50, which includes but is not limited to an OLED display screen, a Mini-LED display screen, or a Micro-LED display screen, etc. For example, in the present application, the LED display screen 50 can be a Mini-LED display screen.

[0098] In some embodiments, the image processing apparatus 10 can be a part of the electronic device 100. Alternatively, the electronic device 100 comprises the image processing apparatus 10.

[0099] In some embodiments, the image processing apparatus 10 can be a discrete element assembled in a certain way to have the aforementioned functions, or a chip in the form of an integrated circuit to have the aforementioned functions, or a computer software code segment to make a computer have the aforementioned functions when running on the computer.

[0100] In some embodiments, as hardware, the image processing apparatus 10 can be independent or added as an additional peripheral element to a computer or a computer system. The image processing apparatus 10 can also be integrated into a computer or a computer system, for example, when the image processing apparatus 10 is a part of the electronic device 100, the image processing apparatus 10 can be integrated into the processor 20.

[0101] In some embodiments in which the image processing apparatus 10 is a part of the electronic device 100, as software, the corresponding code segment of the image processing apparatus 10 can be stored on the storage 30 to be executed by the processor 20 to realize the aforementioned functions. Alternatively, the image processing apparatus 10 comprises the aforementioned one or more programs 32, or the aforementioned one or more programs 32 comprise the image processing apparatus 10.

[0102] In some embodiments, the computer readable storage medium 40 can be a storage medium built in the electronic device 100, for example, the storage 30, or a storage medium pluggable to the electronic device 100, for example, an SD card, etc.

[0103] Please refer to Figure 5 It should be noted that since the RGB mode is a common physical color mode of the LED display 50, that is, usually the image is displayed on the display in the RGB mode, and the display image is used for display in the electronic device 100, the first color data can be RGB color data. That is, the first color data of the pixel point in the original image is RGB color data. RGB is designed from the principle of color light emission, and RGB includes three color channels of red, green and blue, each color is divided into 256 levels of brightness, at 0, the "light" is the weakest - off, and at 255, the "light" is the brightest. When the three color gray scale values are the same, different gray tones of different gray values are generated, that is, when the three color gray scales are all 0, it is the darkest black tone; when the three color gray scales are all 255, it is the brightest white tone. The RGB value refers to the brightness and is represented by an integer. Usually, RGB has 256 levels of brightness, which is represented by numbers from 0, 1, 2... 254, 255.

[0104] It should be further noted that each pixel point comprises a set of first color data, that is, if the first color data is RGB color data, then one pixel point comprises a set of RGB color data.

[0105] Of course, in other embodiments, if the physical color mode of the LED display screen 50 is RGBW mode, then the first color data can also be RGBW color data.

[0106] Those skilled in the relevant art can understand that a color space, also known as a color model (also known as a color space or a color system), is used to describe colors in a generally acceptable manner under certain standards. Color spaces include RGB color space, CMY color space, HSV color space, and HSI color space, etc. Understandably, setting a color space means a pre-defined color space. Since in the present application, it is necessary to convert a color image into an image with only a few colors, converting the first color data into corresponding second color data in the set color space can better process the color data of the pixel points.

[0107] Please further refer to Figure 5 In the present application, the set color space is HSV color space. That is, in the present application, converting the first color data of the pixel points in the original image into corresponding second color data in the set color space means converting the RGB color data of the pixel points in the original image into HSV color data.

[0108] HSV color space is a color space proposed for better digital processing of colors, also known as Hexcone Model. HSV color space defines hue (H), saturation (S), and value (V).

[0109] Among them, hue H is measured by angle, and its value is between 0 degrees and 360 degrees. Starting from red, count counterclockwise, red is 0 degrees, green is 120 degrees, and blue is 240 degrees. Their complementary colors are: yellow is 60 degrees, cyan is 180 degrees, and magenta is 300 degrees.

[0110] Saturation S represents the degree to which a color approaches a spectral color. A color can be considered as the result of mixing a spectral color with white. The greater the proportion of spectral color, the higher the degree to which the color approaches the spectral color, and the higher the saturation of the color. High saturation, color is deep and bright. The white component of the spectral color is 0, and the saturation reaches the highest. Usually the value range is 0%~100%, the larger the value, the more saturated the color.

[0111] Brightness V represents the degree of color brightness. For light source color, the brightness value is related to the brightness of the light source. For object color, the value is related to the transmittance or reflectance of the object. The value is usually in the range of 0% (black) to 100% (white).

[0112] The conversion formula for converting the RGB color data into the HSV color data is as follows:

[0113] V = max (R, G, B)

[0114]

[0115]

[0116] Thus, by the above formula, the first color data of each pixel point in the image data can be converted into the second color data corresponding to the set color space, that is, the RGB color data is converted into the HSV color data.

[0117] It can be understood that, since it is difficult to determine the color represented by the RGB value, the RGB color space does not conform to the user's perception of color. Secondly, the RGB color space is a non-uniform color space, and the perceived difference between two colors cannot be represented by the distance between the two color points in the color space. Since in the HSV color space, the brightness V component is irrelevant to the color information of the image, while the hue H and the saturation S components are relevant to the color information of the image, the HSV color space is suitable for perceiving the color characteristics with the user's visual system. Therefore, converting the RGB color data into the HSV color data can enable the user to intuitively perceive the color characteristics.

[0118] It should be further noted that the feature weighting value is a one-dimensional vector value obtained by quantitatively reducing the dimension of the second color data.

[0119] The preset rule is a rule set in advance, which is used to quantitatively reduce the dimension of the HSV color data of the pixel points in the image to obtain the feature weighting value. The preset rule can be stored in the storage 30 and called by the processor 20 when processing the HSV color data, so as to generate the feature weighting value.

[0120] Further, after obtaining the feature weighting value by quantitatively processing the HSV color data according to the preset rule, the uniformity of the pixel points in the image can be detected according to the feature weighting value, and the uniformity of the pixel points in the image is determined, so that the pixel points can be marked according to the uniformity of the pixel points. In this way, it can be determined whether to reduce the uniformity of some regions to improve the color gamut, so that different uniformity compensation matrices can be configured for each pixel in the subsequent process to improve the color saturation on the basis of ensuring the overall uniformity.

[0121] Please refer toFigure 6 In some embodiments, step S14 comprises sub-steps of:

[0122] S142, pre-processing the second color data to obtain third color data;

[0123] S144, quantizing the third color data according to the color block rule to obtain quantized values corresponding to the hue data, the saturation data and the brightness data; and

[0124] S146, determining the feature weighting values according to the quantized values corresponding to the hue data, the saturation data and the brightness data.

[0125] Please further combine Figure 2 In some embodiments, step S142, step S144 and S146 can be implemented by the computing module 14. Alternatively, the computing module 14 can be configured to pre-process the second color data to obtain third color data. The computing module 14 can also be configured to quantize the third color data according to the color block rule to obtain quantized values corresponding to the hue data, the saturation data and the brightness data, and determine the feature weighting values according to the quantized values corresponding to the hue data, the saturation data and the brightness data.

[0126] Please further combine Figure 3 In some embodiments, the processor 20 can be configured to pre-process the second color data to obtain third color data. The processor 20 can also be configured to quantize the third color data according to the color block rule to obtain quantized values corresponding to the hue data, the saturation data and the brightness data, and determine the feature weighting values according to the quantized values corresponding to the hue data, the saturation data and the brightness data.

[0127] Since the colors of an image are generally very numerous, especially for a true color image, and the calculation amount in the subsequent processing process is large, which can easily lead to a decrease in the accuracy of the color model. Therefore, the processor 20 can pre-process the second color data to regard the colors similar to black as black and the colors similar to white as white, so as to finally obtain third color data. In this way, the number of colors of the image is reduced, the calculation amount in the subsequent processing is reduced, and the accuracy of the color model is improved.

[0128] Specifically, the processor 20 is preset with a first preset value, a second preset value and a third preset value. After the processor 20 obtains the HSV color data of each pixel point, the processor 20 can further obtain the brightness data V and the saturation data S in each HSV color data, and compare the brightness data V with the first preset value and the third preset value respectively and compare the saturation data S with the second preset value. If the brightness data V is less than the first preset value, the HSV color data of the pixel point is mapped as black data (H=0, S=0, V=0), if the saturation data is less than the second preset value and the brightness is greater than the third preset value, the HSV color data is mapped as white data (H=0, S=0, V=1), and the HSV color data of other pixel points remains unchanged, thus the third color data of the set color is obtained.

[0129] For example, in some examples, the first preset value is 0.15, the second preset value is 0.1, the third preset value is 0.8, the first HSV color data is (30, 0.1, 0.1), and the second HSV color data is (60, 0.05, 0.9), then the first HSV color data is mapped as black data (0, 0, 0), and the second HSV color data (60, 0.05, 0.9) is mapped as white data (0, 0, 1).

[0130] Further, according to the color perception characteristics of human vision, the processor 20 performs non-equidistant quantization processing on the hue data H, the saturation data S and the brightness data V of the HSV color data of the pixel points in the image according to the color block rule to obtain the quantized values corresponding to the hue data, the saturation data and the brightness data. The processing formula of the color block rule is as follows:

[0131]

[0132]

[0133]

[0134] Thus, according to the above calculation formula, the quantized values corresponding to the hue data H, the saturation data S and the brightness data V of each pixel point can be obtained. Further, the quantized values corresponding to the hue data, the saturation data and the brightness data are constructed to generate the feature weighting value G of the one-dimensional feature vector. The construction formula is as follows:

[0135] G=HQ S Q V +SQ V +V

[0136] Wherein, Q S is the quantization level of the saturation, Q Vis a quantization level of the brightness data, in the above embodiments, Q S = 3, Q V = 3, that is, in the present application, the feature weighting value G = 9H + 3S + V. In this way, the uniformity of the pixel points in the image can be obtained according to the feature weighting value subsequently.

[0137] Please refer to Figure 11 In some embodiments, the step S16 comprises sub-steps of:

[0138] S161, determining a feature difference between the feature weighting value of the current pixel point and the feature weighting value of the adjacent pixel point;

[0139] S162, marking the current pixel point as a first type of boundary pixel point when the feature difference is greater than a first threshold value;

[0140] S163, marking the current pixel point as a non-boundary pixel point when the feature difference is not greater than the first threshold value.

[0141] In some embodiments, the sub-steps S161, S162 and S163 can be implemented by the determining module 16. In other words, the determining module 16 can be configured to determine the feature difference between the feature weighting value of the current pixel point and the feature weighting value of the adjacent pixel point. The determining module 16 can also be configured to mark the current pixel point as a boundary pixel point when the feature difference is greater than the first threshold value, and mark the current pixel point as a non-boundary pixel point when the feature difference is not greater than the first threshold value.

[0142] In some embodiments, the processor 20 can be configured to determine the feature difference between the feature weighting value of the current pixel point and the feature weighting value of the adjacent pixel point. The processor 20 can also be configured to mark the current pixel point as a boundary pixel point when the feature difference is greater than the first threshold value, and mark the current pixel point as a non-boundary pixel point when the feature difference is not greater than the first threshold value.

[0143] It should be noted that the first threshold value refers to a value preset by the processor 20. The first threshold value can be 1, 2, 3, 4, 5 or a larger value, and the specific size of the first threshold value is not limited.

[0144] Please combine Figure 8Specifically, the processor 20 can traverse the feature weighting values of each pixel point in the image, and compare the feature weighting values between adjacent pixel points to obtain a feature difference value between the adjacent pixel points, and then compare the feature difference value with the first threshold value. If the feature difference value between the current pixel point and the adjacent pixel point is greater than the first threshold value, the pixel point is set as the first type of boundary pixel point. If the feature difference value between the current pixel point and the adjacent pixel point is less than or equal to the first threshold value, the current pixel point can be marked as a non-boundary pixel point. In this way, the first type of boundary pixel point and the non-boundary pixel point can be obtained to obtain a preliminary processed image. It should be noted that in the preliminary processed image as shown in Figure 8 the black marked area is the first type of boundary pixel point, and the white marked area is the non-boundary pixel point.

[0145] Please refer to Figure 9 For example, in some examples, the current pixel point coordinate P1 is (i, j), the coordinates of the adjacent pixels are p2(i+1, j) and p3(i, j+1). If the feature weighting value difference between the current pixel point p1 and one of the pixel points p2 and p3 exceeds the first threshold value, p1 can be marked as the first type of boundary pixel point, otherwise, p1 is marked as a non-boundary pixel point.

[0146] Please refer to Figure 10 In some embodiments, step S16 further comprises a sub-step of:

[0147] S164, determining a neighborhood corresponding to the first type of boundary pixel point, and calculating the number of first type of boundary pixel points in the neighborhood corresponding to any first type of boundary pixel point;

[0148] S165, when the number of first type of boundary pixel points is greater than a second threshold value, marking the first type of boundary pixel point as a first type of non-uniform pixel point;

[0149] S166, when the number of first type of boundary pixel points is not greater than the second threshold value, marking the first type of boundary pixel point and the non-boundary pixel point as a uniform pixel point;

[0150] S167, generating a first uniformity distribution image according to the first type of non-uniform pixel point and the uniform pixel point.

[0151] Please further refer to Figure 2 In some embodiments, steps S164, S165, S166 and step S167 can be implemented by the calculation module 16.

[0152] Alternatively, the computing module 16 can be configured to determine the neighborhood corresponding to the first-type boundary pixel point, and calculate the number of first-type boundary pixel points in the neighborhood corresponding to any first-type boundary pixel point. The computing module 16 can be configured to mark the first-type boundary pixel point as a first-type non-uniform pixel point when the number of first-type boundary pixel points is greater than the second threshold value. The computing module 16 can also be configured to mark the first-type boundary pixel point and the non-boundary pixel point as a uniform pixel point when the number of first-type boundary pixel points is not greater than the second threshold value, and generate the first uniformity distribution image according to the first-type non-uniform pixel point and the uniform pixel point.

[0153] In some embodiments, the processor 20 can be configured to determine the neighborhood corresponding to the first-type boundary pixel point, and calculate the number of first-type boundary pixel points in the neighborhood corresponding to any first-type boundary pixel point. The processor 20 can be configured to mark the first-type boundary pixel point as a first-type non-uniform pixel point when the number of first-type boundary pixel points is greater than the second threshold value. The processor 20 can also be configured to mark the first-type boundary pixel point and the non-boundary pixel point as a uniform pixel point when the number of first-type boundary pixel points is not greater than the second threshold value, and generate the first uniformity distribution image according to the first-type non-uniform pixel point and the uniform pixel point.

[0154] It should be noted that the neighborhood refers to a basic topological structure on a set, and the neighborhood corresponding to the first-type boundary pixel point refers to an interval of a predetermined range centered on the first-type boundary pixel point. The neighborhood range can be adjusted and set.

[0155] The second threshold value refers to a positive integer preset by the processor 20, which is used for comparison with the number of first-type boundary pixel points. The second threshold value can be 5, 8, 10, 12, or even 20 or more, and the specific value of the second threshold value is not limited.

[0156] For example, the second threshold value can be 8. Please refer to Figure 11 , the square represents the first-type boundary pixel point, and the circle represents the non-boundary pixel point. For the first-type boundary pixel point P2, the neighborhood range of P2 is determined by taking the 7*7 pixel points around P2 in the form of a square with P2 as the center. At this time, the number of first-type boundary pixel points in the neighborhood range of the first-type boundary pixel point P2 is 15, that is, the number of first-type boundary pixel points in the neighborhood range of the first-type boundary pixel point P2 is greater than the second threshold value, and the first-type boundary pixel point P2 is marked as a first-type non-uniform pixel point. Correspondingly, for the case that the number of first-type boundary pixel points in the neighborhood range of the first-type boundary pixel point P2 is not greater than the second threshold value, the first-type boundary pixel point P2 can be marked as a uniform pixel point. In this way, all first-type boundary pixel points on the preliminary processing image are iterated in turn, and the first-type boundary pixel points and the non-boundary pixel points can be classified to obtain the first-type non-uniform pixel point and the uniform pixel point.

[0157] It should be noted that in step S164, the neighborhood of the first type of boundary pixel point is obtained on the basis of the preliminary processed image, therefore, the first type of non-uniform pixel point or the uniform pixel point marked through steps S165 and S166 will not affect the marking of other first type of boundary pixel points.

[0158] Please refer to Figure 12 In this way, after determining the first type of non-uniform pixel point and the uniform pixel point of the pixel point in the image, the preliminary processed image can be generated into a first uniformity distribution image. It should be noted that in the first uniformity distribution image as shown in Figure 11 , the black marked area is the uniform pixel point, and the white marked area is the first type of non-uniform pixel point.

[0159] Please refer to Figure 13 In some embodiments, step S16 further comprises a sub-step of:

[0160] S168, performing dilation processing on the first uniformity distribution image to obtain the second type of non-uniform pixel point, and generating a second uniformity distribution image;

[0161] S169, in the second uniformity distribution image, marking the second type of non-uniform pixel point whose adjacent pixel points in a preset direction are uniform pixel points as the second type of boundary pixel point;

[0162] S160, in the row and / or column where the second type of boundary pixel point is located, marking the second type of boundary pixel point and the preset number of pixel points adjacent to the second type of boundary pixel point as transition pixel points, marking the second type of non-uniform pixel point that is not a transition pixel point as a target non-uniform pixel point, and marking the uniform pixel point that is not a transition pixel point and is not a second type of non-uniform pixel point as a target uniform pixel point.

[0163] Please further refer to Figure 2 In some embodiments, steps S168, S169 and S160 are realized by a calculation module 16.

[0164] In other words, the calculation module 16 can be used to perform dilation processing on the uniformity distribution image to obtain a second uniformity distribution image, and in the second uniformity distribution image, mark the non-uniform pixel point whose adjacent pixel points in a preset direction are uniform pixel points as a target boundary pixel point; the calculation module 16 can also be used to, in the row and / or column where the second type of boundary pixel point is located, mark the second type of boundary pixel point and the preset number of pixel points adjacent to the second type of boundary pixel point as transition pixel points, mark the second type of non-uniform pixel point that is not a transition pixel point as a target non-uniform pixel point, and mark the uniform pixel point that is not a transition pixel point and is not a second type of non-uniform pixel point as a target uniform pixel point.

[0165] In some embodiments, the processor 20 can be configured to dilate the first uniformity distribution image to obtain a second uniformity distribution image, and in the second uniformity distribution image, a second type of non-uniform pixel whose adjacent pixels in a preset direction are uniform pixels is a target boundary pixel point. The processor 20 can also be configured to mark, in the row and / or column where the second type of boundary pixel point is located, the second type of boundary pixel point and a preset number of pixel points adjacent to the second type of boundary pixel point as transition pixel points, mark the second type of non-uniform pixel point that is not a transition pixel point as a target non-uniform pixel point, and mark the uniform pixel point that is not a transition pixel point and is not a second type of non-uniform pixel point as a target uniform pixel point.

[0166] Specifically, referring to Figure 14 , in the distribution area of the uniform pixel points of the first uniformity distribution image, there are many discrete first type of non-uniform pixel points (discrete pixel points), and the discrete pixel points affect the uniformity of the image. Therefore, the uniformity distribution image can be dilated to obtain a second uniformity distribution image, so that the area of the non-uniform pixel points and the area of the uniform pixel points are obviously different.

[0167] It should be noted that the dilation processing refers to dilating (expanding the field) the highlight part of the image, so that the effect picture has a larger highlight area than the original picture. In this application, after dilating the uniform pixel points of the first uniformity distribution image, the discrete first type of non-uniform pixel points and part of the uniform pixel points can be converted into the second type of non-uniform pixel points, and the second uniformity distribution image is generated.

[0168] Further, after obtaining the second uniformity distribution image, the processor 20 can traverse the second uniformity distribution image, and if the adjacent pixel to the right or below the second type of non-uniform pixel is a uniform pixel point, then the second type of non-uniform pixel is determined as a second type of boundary pixel point.

[0169] Further, in the row where the second type of boundary pixel point is located, the second type of boundary pixel point and a preset number of pixel points adjacent to the second type of boundary pixel point are set as transition pixel points, the second type of non-uniform pixel point that is not a transition pixel point is marked as a target non-uniform pixel point, and the uniform pixel point that is not a transition pixel point and is not a second type of non-uniform pixel point is marked as a target uniform pixel point.

[0170] Alternatively, in the column where the second type of boundary pixel point is located, the second type of boundary pixel point and a preset number of pixel points adjacent to the second type of boundary pixel point are set as transition pixel points, the second type of non-uniform pixel point that is not a transition pixel point is marked as a target non-uniform pixel point, and the uniform pixel point that is not a transition pixel point and is not a second type of non-uniform pixel point is marked as a target uniform pixel point.

[0171] Or, in the row and column where the second type of boundary pixel point is located, the second type of boundary pixel point and a preset number of pixel points adjacent to the second type of boundary pixel point are set as transition pixel points, and the second type of non-uniform pixel point which is not a transition pixel point is marked as a target non-uniform pixel point, and the uniform pixel point which is not a transition pixel point and is not a second type of non-uniform pixel point is marked as a target uniform pixel point.

[0172] In the process of setting the second type of boundary pixel point and a preset number of pixel points adjacent to the second type of boundary pixel point as transition pixel points, the preset number of pixel points adjacent to the second type of boundary pixel point are marked as transition pixel points regardless of whether they are second type of non-uniform pixel points or uniform pixel points.

[0173] In addition, it should be noted that the preset number can be a positive integer randomly selected from a preset range. For example, the preset range is [1, 10], and the preset number can be any one of the ten positive integers from 1 to 10.

[0174] In this way, the unnatural transition phenomenon of the boundary between the uniform pixel point region and the non-uniform pixel point region can be reduced. Moreover, since the distribution of the pixel points in the original image corresponds to the distribution of the pixel points in the second uniformity distribution image, the pixel points in the original image can also be marked as target uniform pixel points, target non-uniform pixel points, and transition pixel points. Randomly selecting the preset number from the preset range can also reduce the problem of too obvious transition region.

[0175] Please refer to Figure 15 In some embodiments, the image processing method further comprises the steps of:

[0176] S17, determining a uniformity compensation matrix according to the uniformity of the pixel points;

[0177] S18, processing the pixel points according to the uniformity compensation matrix to obtain a display image.

[0178] Please further refer to Figure 2 In some embodiments, the image processing device further comprises a processing module 18. Step S17 can be implemented by the determination module 14, and step S18 can be implemented by the processing module 18.

[0179] In other words, the determination module 14 can be configured to determine a uniformity compensation matrix according to the uniformity of the pixel points.

[0180] The processing module 18 can be configured to process the pixel points according to the uniformity compensation matrix to obtain a display image.

[0181] In some embodiments, the processor 20 can be configured to determine the uniformity compensation matrix according to the uniformity of the pixel points, and the processor 20 can be further configured to process the pixel points according to the uniformity compensation matrix to obtain the display image.

[0182] It should be noted that the uniformity compensation matrix is a transformation matrix, which is used to process the RGB color data of the pixel points in the original image to generate the display image.

[0183] The uniformity compensation matrix can include multiple uniformity compensation matrices, and the multiple uniformity compensation matrices can be used to process the target uniform pixel points, the target non-uniform pixel points, and the transition pixel points in the original image respectively to obtain the display image. It can be understood that the second uniformity distribution image obtained after the above processing includes the target uniform pixel points, the target non-uniform pixel points, and the transition pixel points, that is, the second uniformity distribution image is divided into three distribution regions, and the second uniformity distribution image is generated by processing the original image, and the pixel points of the second uniformity distribution image correspond to the pixel points of the original image. Therefore, different uniformity compensation matrices (color gamut mapping) are used to process the corresponding pixel points in the original image according to the pixel uniformity classification of the second uniformity distribution image, so as to obtain the display image. In this way, the display image can improve the color saturation and enhance the visual effect on the basis of ensuring the overall uniformity.

[0184] Please refer to Figure 16 In some embodiments, the step S17 further includes the following sub-steps:

[0185] S172, obtaining the first color gamut conversion matrix corresponding to the target non-uniform pixel points, the second color gamut conversion matrix corresponding to the transition pixel points, and the third color gamut conversion matrix corresponding to the target uniform pixel points respectively, and obtaining the display pixel conversion matrix of the light emitting element corresponding to each pixel point on the display panel.

[0186] The step S18 further includes the following sub-steps:

[0187] S182, processing the corresponding target non-uniform pixel points according to the first color gamut conversion matrix and the display pixel conversion matrix;

[0188] S184, processing the corresponding transition pixel points according to the second color gamut conversion matrix and the display pixel conversion matrix;

[0189] S186, processing the corresponding target uniform pixel points according to the third color gamut conversion matrix and the display pixel conversion matrix.

[0190] In some embodiments, the sub-steps S172, S182, S184, and S186 can be implemented by the processing module 18.

[0191] Alternatively, the processing module 18 can be configured to obtain a first color gamut conversion matrix corresponding to the target non-uniform pixel, a second color gamut conversion matrix corresponding to the transition pixel, and a third color gamut conversion matrix corresponding to the target uniform pixel, respectively, and obtain a display pixel conversion matrix of the light emitting element on the display panel corresponding to each pixel.

[0192] The processing module 18 can be further configured to process the corresponding target non-uniform pixel according to the first color gamut conversion matrix and the display pixel conversion matrix, or process the corresponding transition pixel according to the second color gamut conversion matrix and the display pixel conversion matrix, and the processing module 18 can also process the corresponding target non-uniform pixel according to the first color gamut conversion matrix and the display pixel conversion matrix.

[0193] In some embodiments, the processor 20 is configured to obtain a first color gamut conversion matrix corresponding to the target non-uniform pixel, a second color gamut conversion matrix corresponding to the transition pixel, and a third color gamut conversion matrix corresponding to the target uniform pixel, respectively, and obtain a display pixel conversion matrix of the light emitting element on the display panel corresponding to each pixel. The processor 20 can be further configured to process the corresponding target non-uniform pixel according to the first color gamut conversion matrix and the display pixel conversion matrix, or process the corresponding transition pixel according to the second color gamut conversion matrix and the display pixel conversion matrix, and the processor 20 can also process the corresponding target non-uniform pixel according to the first color gamut conversion matrix and the display pixel conversion matrix.

[0194] It should be noted that the first color gamut is a color gamut corresponding to the target non-uniform pixel, the second color gamut is a color gamut corresponding to the transition pixel, and the third color gamut is a color gamut corresponding to the target uniform pixel.

[0195] Specifically, the uniformity compensation matrix includes a first color gamut conversion matrix, a second color gamut conversion matrix, a third color gamut conversion matrix, and a display pixel conversion matrix. The first color gamut conversion matrix corresponds to the first color gamut, the second color gamut conversion matrix corresponds to the second color gamut, and the third color gamut conversion matrix corresponds to the third color gamut. The range of the first color gamut is greater than the range of the second color gamut, the range of the second color gamut is greater than the range of the third color gamut, and the first color gamut, the second color gamut, and the third color gamut all belong to the display color gamut.

[0196] The first color gamut conversion matrix is used to convert the RGB color data of the target non-uniform pixel from the input color space to the first color gamut space, that is, to convert the RGB color data of the target non-uniform pixel into the first color gamut. The second color gamut conversion matrix is used to convert the RGB color data of the transition pixel from the input color space to the second color gamut space, that is, to convert the RGB color data of the transition pixel into the second color gamut. The third color gamut conversion matrix is used to convert the RGB color data of the target uniform pixel from the input color space to the third color gamut space, that is, to convert the RGB color data of the target uniform pixel into the third color gamut.

[0197] Further, the processor can determine the first color gamut conversion matrix according to the color coordinates and luminance of the three primary colors in the first color gamut, determine the second color gamut conversion matrix according to the color coordinates and luminance of the three primary colors in the second color gamut, and determine the third color gamut conversion matrix according to the color coordinates and luminance of the three primary colors in the third color gamut.

[0198] The formula of the first color gamut conversion matrix is:

[0199]

[0200] wherein C1 refers to a large color gamut conversion matrix, (R in , G in , B in ) is the input RGB color data. (X, Y, Z) is the first color gamut color data.

[0201] The formula of the second color gamut conversion matrix is:

[0202]

[0203] wherein C2 refers to a transition color gamut conversion matrix, (R in , G in , B in ) is the input RGB color data. (X, Y, Z) is the second color gamut color data.

[0204] The formula of the third color gamut conversion matrix is:

[0205]

[0206] wherein C3 refers to a small color gamut conversion matrix, (R in , G in , B in ) is the input RGB color data. (X, Y, Z) is the third color gamut color data.

[0207] The display pixel conversion matrix is used to convert the input RGB color data from the first color gamut space, the second color gamut space or the third color gamut space to the display color gamut space. It can be understood that, since the first color data of the original image is RGB color data, the input color space is RGB color space, and the display image is also displayed on the LED display screen 50, and the display color gamut space is also RGB color space.

[0208] Further, the corresponding display pixel conversion matrix is determined according to the RGB color data of the light emitting element on the display panel corresponding to each pixel point. The RGB color data of the light emitting element on the display panel can be the color coordinates and the brightness of the light emitting element.

[0209] The display pixel conversion matrix formula is:

[0210]

[0211] wherein, is the inverse conversion matrix of the display color gamut, (R out , G out , B out ) is the display color data. (X, Y, Z) is the color gamut color data.

[0212] It should be noted that the conversion matrix C xianshi of the display color gamut is:

[0213]

[0214] wherein, the color coordinates of R in the RGB color data of the pixel point are (x r0 , y r0 ), the brightness is Y r0 , the color coordinates of B in the RGB color data of the pixel point are (x g0 , y g0 ), the brightness Y r0 , the color coordinates of G in the RGB color data of the pixel point are (x b0 , y b0 ), and the brightness is Y b0 .

[0215] The calculation formula of C1, C2 and C3 is:

[0216]

[0217] wherein, i is 1, 2, 3, the color coordinates of R in the corresponding color gamut primary color are (x ri , y ri ), the brightness is Y ri , the color coordinates of B in the RGB color data of the pixel point are (x gi , y gi), brightness is Y gi In the RGB color data of a pixel, the color coordinates of G are (x... bi y bi ), brightness is Y bi .

[0218] For example, when i = 1, that is, when the first color gamut transformation matrix C1 is:

[0219]

[0220] Among them, the color coordinates of R corresponding to the three primary colors of the color gamut are (x... r1 y r1 ), brightness is Y r1 The color coordinates of B corresponding to the three primary colors of the first color gamut are (x... g1 y g1 ), brightness is Y g1 In the RGB color data of a pixel, the color coordinates of G are (x... b1 y b1 ), brightness is Y b1 .

[0221] Thus, by using a uniformity compensation matrix, the uniformity of certain areas can be reduced to improve the color gamut. This allows for configuring different uniformity compensation matrices for each pixel, improving color saturation while maintaining overall uniformity. For example, the processor 20 can obtain a first color gamut conversion matrix and a display pixel conversion matrix, and process the target non-uniform pixels according to these matrices to obtain a display image. Alternatively, it can obtain a second color gamut conversion matrix and a display pixel conversion matrix, and process transitional pixels according to these matrices to obtain a display image. Or, it can obtain a third color gamut conversion matrix and a display pixel conversion matrix, and process the target uniform pixels according to these matrices to obtain a display image.

[0222] Furthermore, after mapping to different color gamuts, the image hue remains unchanged, only the saturation changes. White point correction can be performed separately for large color gamuts, small color gamuts, and transitional color gamuts. The white point correction method is as follows:

[0223]

[0224] The 3x3 matrix above is the color gamut conversion matrix without white point correction, where X is the standard white point. W Y W Z W Given that the correction coefficients are known, the transformation matrix after white point correction can be calculated as follows:

[0225]

[0226] In some embodiments, the image processing method further comprises the steps of: Figure 17 In some embodiments, the image processing method further comprises the steps of:

[0227] S19, determining a third color gamut according to the common color gamut range of all the light emitting elements;

[0228] S21, determining a second color gamut according to the third color gamut and the first proportion;

[0229] S23, determining a first color gamut according to the third color gamut and a second proportion, the second proportion being greater than the first proportion.

[0230] In some embodiments, the steps S19, S21 and S23 can be implemented by the processing module 18.

[0231] In other words, the processing module 18 is configured to determine a third color gamut according to the common color gamut range of all the light emitting elements.

[0232] The processing module 18 is configured to determine a second color gamut according to the third color gamut and the first proportion.

[0233] The processing module 18 is further configured to determine a first color gamut according to the third color gamut and a second proportion, the second proportion being greater than the first proportion.

[0234] In some embodiments, the processor 20 is configured to determine a third color gamut according to the common color gamut range of all the light emitting elements. The processor 20 is configured to determine a second color gamut according to the third color gamut and the first proportion. The processor 20 is further configured to determine a first color gamut according to the third color gamut and a second proportion, the second proportion being greater than the first proportion.

[0235] It should be noted that the light emitting elements refer to the pixel points in the LED display screen. It should be noted that each light emitting element is configured to display any one value of the R color data, the G color data and the B color data in the RGB color data. It can be understood that there will be differences in brightness and chromaticity among the light emitting elements in each LED display screen 50, so that different LED display screens 50 will have deviations when displaying the same image. Therefore, the light emitting elements in the LED display screen 50 need to be detected to determine the first color gamut, the second color gamut and the third color gamut range.

[0236] In some embodiments, the image processing method further comprises the steps of: Figure 18 Specifically, the processor 20 can input a pure color image to the LED display screen 50, so that the LED display screen 50 displays the pure color image, and detect the color coordinates (x, y) and the brightness value of each light emitting element under the D65 color standard, to determine the brightness of each light emitting element displaying the R color data, the brightness of each light emitting element displaying the G color data and the brightness of each light emitting element displaying the B color data.

[0237] Further, the processor 20 can compare the brightness of all light emitting elements displaying R color data, obtain a first target light emitting element displaying R color data with the minimum brightness value, compare the brightness of all light emitting elements displaying G color data, obtain a second target light emitting element displaying G color data with the minimum brightness value, and compare the brightness of all light emitting elements displaying B color data, obtain a third target light emitting element displaying B color data with the minimum brightness value. Then the color coordinates of the first target light emitting element, the second target light emitting element, and the third light emitting element are determined. Finally, a color gamut triangle is obtained according to the color coordinates of the first target light emitting element, the second target light emitting element, and the third light emitting element, and the third color gamut is set as the area of the color gamut triangle.

[0238] In addition, the processor 20 is further provided with a first ratio and a second ratio, wherein the first ratio and the second ratio are greater than 1, and the second ratio is greater than the first ratio, for example, the first ratio can be 1.2, and the second ratio can be 1.4. The processor 20 can connect the D65 white point color coordinates with the color coordinates of the first target light emitting element, the color coordinates of the second target light emitting element, and the color coordinates of the third light emitting element to obtain a first connecting line, a second connecting line, and a third connecting line. Then, the first connecting line, the second connecting line, and the third connecting line are extended in the direction of the first connecting line, the second connecting line, and the third connecting line according to the first ratio, to obtain a first color coordinate, a second color coordinate, and a third color coordinate, and the first color coordinate, the second color coordinate, and the third color coordinate are sequentially connected to obtain a new color gamut triangle, and the second color gamut is set as the area of the color gamut triangle.

[0239] Further, the processor 20 further extends the first connecting line, the second connecting line, and the third connecting line in the direction of the first connecting line, the second connecting line, and the third connecting line according to the second ratio, to obtain a fourth color coordinate, a fifth color coordinate, and a sixth color coordinate, and sequentially connects the fourth color coordinate, the fifth color coordinate, and the sixth color coordinate to obtain a new color gamut triangle, and the third color gamut is set as the area of the color gamut triangle.

[0240] In this way, the first color gamut area range, the second color gamut area range, and the third color gamut area range are obtained.

[0241] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiment is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0242] In addition, each function unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit.

[0243] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An image processing method, characterized in that, include: Acquire the original image and convert the first color data of the pixels in the original image into the second color data corresponding to the set color space; The feature weighting value corresponding to the pixel is calculated based on the second color data and the preset rules. The feature weighting value is determined based on the quantization value corresponding to the hue data, saturation data and brightness data. and The uniformity of the pixels is determined based on the weighted feature values ​​to mark the pixels; The step of determining the uniformity of the pixel based on the feature weighting value to mark the pixel includes: Determine the feature difference between the feature weight value of the current pixel and the feature weight values ​​of its neighboring pixels; When the feature difference is greater than the first threshold, the current pixel is marked as a first-class boundary pixel. When the feature difference is not greater than the first threshold, the current pixel is marked as a non-boundary pixel. The step of determining the uniformity of the pixel based on the feature weight value to mark the pixel further includes: Determine the neighborhood corresponding to the first type of boundary pixel, and calculate the number of first type boundary pixels within the neighborhood corresponding to any first type of boundary pixel; When the number of boundary pixels of the first type is greater than the second threshold, the boundary pixels of the first type are marked as non-uniform pixels of the first type; and When the number of the first type of boundary pixels is not greater than the second threshold, the first type of boundary pixels and the non-boundary pixels are marked as uniform pixels. A first uniform distribution image is generated based on the first type of non-uniform pixels and the uniform pixels.

2. The image processing method according to claim 1, characterized in that, The color space is set to HSV color space.

3. The image processing method according to claim 2, characterized in that, The second color data includes hue data, saturation data, and brightness data. The step of calculating the feature weighting value corresponding to the pixel based on the second color data and preset rules includes: The second color data is preprocessed to obtain the third color data; The third color data is quantized according to color block rules to obtain quantized values ​​corresponding to the hue data, saturation data, and brightness data; and The feature weighting value is determined based on the quantization values ​​corresponding to the hue data, the saturation data, and the brightness data.

4. The image processing method according to claim 3, characterized in that, The preprocessing of the second color data to obtain the third color data includes: The second color data whose brightness data is less than a first preset value is determined to be black data; and The second color data, where the saturation data is less than a second preset value and the brightness data is greater than a third preset value, is determined to be white data.

5. The image processing method according to claim 1, characterized in that, The step of determining the uniformity of the pixel based on the feature weighting value to mark the pixel further includes: The first uniformly distributed image is dilated to obtain a second type of non-uniform pixel points, and a second uniformly distributed image is generated. In the second uniform distribution image, the second type of non-uniform pixels whose adjacent pixels along the preset direction are the uniform pixels are marked as the second type of boundary pixels; In the row and / or column where the second type of boundary pixel is located, the second type of boundary pixel and a preset number of pixels adjacent to the second type of boundary pixel are marked as transition pixels, the second type of non-uniform pixels that are not the transition pixels are marked as target non-uniform pixels, and the uniform pixels that are neither the transition pixels nor the second type of non-uniform pixels are marked as target uniform pixels.

6. The image processing method according to claim 5, characterized in that, Marking the second type of boundary pixels and a predetermined number of pixels adjacent to the second type of boundary pixels as transition pixels includes: Randomly select a value within a preset range to obtain the preset quantity.

7. The image processing method according to claim 5, characterized in that, The image processing method further includes: Determine the uniformity compensation matrix based on the uniformity of the pixels; The pixels are processed according to the uniformity compensation matrix to obtain the displayed image.

8. The image processing method according to claim 7, characterized in that, The step of determining the uniformity compensation matrix based on the uniformity of the pixels includes: The first color gamut conversion matrix corresponding to the target non-uniform pixel, the second color gamut conversion matrix corresponding to the transition pixel, and the third color gamut conversion matrix corresponding to the target uniform pixel are obtained respectively. The display pixel conversion matrix of the light-emitting element on the display panel corresponding to each pixel is also obtained. The step of processing the pixels according to the uniformity compensation matrix to obtain the displayed image includes: The corresponding target non-uniform pixels are processed according to the first color gamut conversion matrix and the display pixel conversion matrix; The corresponding transition pixels are processed according to the second color gamut conversion matrix and the display pixel conversion matrix; and The corresponding target uniform pixels are processed according to the third color gamut conversion matrix and the display pixel conversion matrix.

9. The image processing method according to claim 8, characterized in that, The first color gamut conversion matrix corresponds to the first color gamut, the second color gamut conversion matrix corresponds to the second color gamut, and the third color gamut conversion matrix corresponds to the third color gamut. The range of the first color gamut is greater than the range of the second color gamut, and the range of the second color gamut is greater than the range of the third color gamut.

10. The image processing method according to claim 9, characterized in that, Image processing methods include: The third color gamut is determined based on the common color gamut range of all light-emitting elements; The second color gamut is determined based on the third color gamut and the first ratio; and The first color gamut is determined based on the third color gamut and the second ratio, where the second ratio is greater than the first ratio.

11. The image processing method according to claim 9, characterized in that, The step of obtaining the first color gamut conversion matrix corresponding to the target non-uniform pixel, the second color gamut conversion matrix corresponding to the transition pixel, and the third color gamut conversion matrix corresponding to the target uniform pixel includes: The first color gamut transformation matrix is ​​determined based on the color coordinates and brightness of the three primary colors in the first color gamut. The second color gamut transformation matrix is ​​determined based on the color coordinates and brightness of the three primary colors in the second color gamut; and The third color gamut transformation matrix is ​​determined based on the color coordinates and brightness of the three primary colors in the third color gamut.

12. The image processing method according to claim 8, characterized in that, The step of obtaining the display pixel transformation matrix of the light-emitting element on the display panel corresponding to each pixel includes: The corresponding display pixel transformation matrix is ​​determined based on the RGB color data of the light-emitting element on the display panel corresponding to each pixel.

13. An image processing apparatus, characterized in that, include: The acquisition module can be used to acquire the original image and convert the first color data of the pixels in the original image into second color data corresponding to a set color space; The calculation module can be used to calculate the feature weighting value corresponding to the pixel based on the second color data and preset rules. The feature weighting value is determined based on the quantization value corresponding to the hue data, saturation data and brightness data. and The determining module can be used to determine the uniformity of the pixel based on the feature weighting value in order to mark the pixel; The determining module can also be used to determine the feature difference between the feature weighted value of the current pixel and the feature weighted values ​​of the adjacent pixels; when the feature difference is greater than a first threshold, the current pixel is marked as a first-class boundary pixel; when the feature difference is not greater than the first threshold, the current pixel is marked as a non-boundary pixel. The calculation module can also be used to determine the neighborhood corresponding to the first type of boundary pixel, and calculate the number of first type boundary pixels in the neighborhood corresponding to any first type of boundary pixel; when the number of first type boundary pixels is greater than a second threshold, mark the first type of boundary pixels as first type non-uniform pixels; and when the number of first type boundary pixels is not greater than the second threshold, mark the first type of boundary pixels and the non-boundary pixels as uniform pixels; and generate a first uniformity distribution image based on the first type of non-uniform pixels and the uniform pixels.

14. An electronic device, characterized in that, include: One or more processors or memories; and One or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, the programs comprising instructions for performing the image processing method according to any one of claims 1-12.

15. A non-volatile computer-readable storage medium for a computer program, characterized in that, When the computer program is executed by one or more processors, the processors perform the image processing method according to any one of claims 1-12.

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