Image Adjustment Method, Device, Chip, Display Device, and Electronic Device

Abstract

The present disclosure relates to an image adjustment method, apparatus, chip, display device, and electronic device. The adjustment method includes: obtaining an image to be processed; dividing the image to be processed into a plurality of sub-images to be processed; for each preset gray level among a plurality of preset gray levels in each sub-image to be processed, determining a dynamic contrast ratio; determining a statistical feature corresponding to each sub-image to be processed, and determining a first brightness value of each preset gray level according to the statistical feature and the dynamic contrast ratio; determining a gray space distribution feature corresponding to each sub-image to be processed, and determining a second brightness value of each preset gray level according to the gray space distribution feature and the first brightness value corresponding to each preset gray level in each sub-image to be processed; determining a brightness value corresponding to each gray level according to the second brightness value, and adjusting the brightness of pixel points corresponding to each gray level according to the brightness value corresponding to each gray level. The embodiments of the present disclosure can save the computing resources of the chip.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing, and in particular, to an image adjustment method, apparatus, chip, display device, and electronic device. Background Art

[0002] With the development of display devices, more display devices begin to support the function of adjusting dynamic contrast. Dynamic contrast refers to the contrast value measured under certain specific conditions. For example, if the brightness of a full-white screen is 200 candela per square meter and the brightness of a full-black screen is 0.1 candela per square meter, then the dynamic contrast value is 2000:1. Dynamic contrast has obvious practical significance in the case of frequent switching between bright and dark rendering scenes. Therefore, how to better adjust the brightness value of an image through dynamic contrast is a technical problem that those skilled in the art urgently need to solve. Summary of the Invention

[0003] In view of this, the present disclosure proposes a technical solution for image adjustment.

[0004] According to an aspect of the present disclosure, there is provided an image adjustment method, the adjustment method including: obtaining an image to be processed; dividing the image to be processed into a plurality of sub-images to be processed according to a preset first segmentation rule; for each preset gray level among a plurality of preset gray levels in each sub-image to be processed, determining the dynamic contrast corresponding to each preset gray level; determining the statistical feature corresponding to each sub-image to be processed, and determining the first brightness value corresponding to each preset gray level in each sub-image to be processed according to the statistical feature corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed, where the statistical feature is used to represent the difference between the dynamic contrasts corresponding to each preset gray level in the same sub-image to be processed; determining the gray space distribution feature corresponding to each sub-image to be processed, and determining the second brightness value corresponding to each preset gray level in each sub-image to be processed according to the gray space distribution feature corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed; determining the brightness value corresponding to each gray level in each sub-image to be processed according to the second brightness value corresponding to each preset gray level in each sub-image to be processed and the magnitude relationship between each gray level and each preset gray level in each sub-image to be processed, and adjusting the brightness of the pixel points corresponding to each gray level in each sub-image to be processed according to the brightness value corresponding to each gray level in each sub-image to be processed.

[0005] In a possible implementation manner, determining the grayscale spatial distribution feature corresponding to each sub-image to be processed includes: dividing each sub-image to be processed into multiple regional images according to a preset second segmentation rule; for each regional image corresponding to each sub-image to be processed, performing at least two cropping operations on each regional image corresponding to each sub-image to be processed according to a preset third segmentation rule to obtain at least one set of cropped images for each regional image corresponding to each sub-image to be processed; wherein, each set of cropped images includes two cropped images, and the number of pixel points in each row and each column between each pair of cropped images in the same set of cropped images is equal; determining the grayscale difference between the two cropped images in each set of cropped images for each regional image corresponding to each sub-image to be processed; determining the grayscale spatial distribution feature of each regional image corresponding to each sub-image to be processed according to the grayscale difference between the two cropped images in each set of cropped images for each regional image corresponding to each sub-image to be processed; and performing feature fusion on the grayscale spatial distribution features of each regional image corresponding to each sub-image to be processed to obtain the grayscale spatial distribution feature corresponding to each sub-image to be processed.

[0006] In a possible implementation manner, according to a preset third segmentation rule, each regional image corresponding to each sub-image to be processed is cropped at least twice to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed, including at least one of the following: cropping the pixel points of the first preset number of rows from the first row to the last row of each regional image corresponding to each sub-image to be processed to obtain a first image of each regional image corresponding to each sub-image to be processed; cropping the pixel points of the first preset number of rows from the last row to the first row of each regional image corresponding to each sub-image to be processed to obtain a second image of each regional image corresponding to each sub-image to be processed; using the first image and the second image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed; cropping the pixel points of the first preset number of columns from the first column to the last column of each regional image corresponding to each sub-image to be processed to obtain a third image of each regional image corresponding to each sub-image to be processed; cropping the pixel points of the first preset number of columns from the last column to the first column of each regional image corresponding to each sub-image to be processed to obtain a fourth image of each regional image corresponding to each sub-image to be processed; using the third image and the fourth image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed; cropping the pixel points of the second preset number of rows from the first row to the last row and the second preset number of columns from the first column to the last column of each regional image corresponding to each sub-image to be processed to obtain a fifth image of each regional image corresponding to each sub-image to be processed; cropping the pixel points of the second preset number of rows from the last row to the first row and the second preset number of columns from the last column to the first column of each regional image corresponding to each sub-image to be processed to obtain a sixth image of each regional image corresponding to each sub-image to be processed; using the fifth image and the sixth image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed.

[0007] In a possible implementation manner, determining the statistical features corresponding to each sub-image to be processed includes: determining the average value and the standard deviation of the gray values corresponding to each preset gray level in each sub-image to be processed, and using the ratio between the standard deviation and the average value as the statistical feature corresponding to each sub-image to be processed.

[0008] In a possible implementation, determining the dynamic contrast corresponding to each preset gray level includes: determining a gray level histogram corresponding to the sub-image to be processed; wherein, the gray level histogram is used to represent the number of pixel points in the sub-image to be processed distributed on the multiple preset gray levels; adjusting the number of pixel points of each preset gray level in the gray level histogram whose number of pixel points is greater than a preset threshold to the preset threshold; determining a cumulative distribution histogram corresponding to the sub-image to be processed according to the adjusted gray level histogram; wherein, the cumulative distribution histogram is used to represent the total number of pixel points whose gray level values are less than or equal to each preset gray level; determining the dynamic contrast corresponding to each preset gray level in the sub-image to be processed according to the cumulative distribution histogram.

[0009] According to another aspect of the present disclosure, there is provided an image adjustment device, the adjustment device includes: an image acquisition module for acquiring an image to be processed; an image division module for dividing the image to be processed into multiple sub-images to be processed according to a preset first segmentation rule; a dynamic contrast determination module for determining the dynamic contrast corresponding to each preset gray level among multiple preset gray levels in each sub-image to be processed; a first brightness value determination module for determining the statistical characteristics corresponding to each sub-image to be processed, and determining the first brightness value corresponding to each preset gray level in each sub-image to be processed according to the statistical characteristics corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed; wherein, the statistical characteristics are used to represent the difference between the dynamic contrasts corresponding to each preset gray level in the same sub-image to be processed; a second brightness value determination module for determining the gray level spatial distribution characteristics corresponding to each sub-image to be processed, and determining the second brightness value corresponding to each preset gray level in each sub-image to be processed according to the gray level spatial distribution characteristics corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed; a brightness adjustment module for determining the brightness value corresponding to each gray level in each sub-image to be processed according to the second brightness value corresponding to each preset gray level in each sub-image to be processed and the size relationship between each gray level and each preset gray level in each sub-image to be processed, and adjusting the brightness of the pixel points corresponding to each gray level in each sub-image to be processed according to the brightness value corresponding to each gray level in each sub-image to be processed.

[0010] According to another aspect of the present disclosure, there is provided a chip for executing the image adjustment method according to any one of the above.

[0011] According to another aspect of the present disclosure, there is provided a display device including a plurality of display units and at least one of the above adjustment devices, or including the chip.

[0012] In a possible implementation, the display unit includes a display panel, and the display panel includes at least one of a liquid crystal display panel, a micro light-emitting diode display panel, a light-emitting diode display panel, a mini light-emitting diode display panel, a quantum dot light-emitting diode display panel, an organic light-emitting diode display panel, a cathode ray tube display panel, a digital light processing display panel, a field emission display panel, a plasma display panel, an electrophoretic display panel, an electro-wetting display panel, and a small pitch display panel.

[0013] According to another aspect of the present disclosure, an electronic device is provided, including the display device described above.

[0014] According to another aspect of the present disclosure, an image adjustment device is provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-mentioned image adjustment method when executing the instructions stored in the memory.

[0015] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned method is implemented.

[0016] According to another aspect of the present disclosure, a computer program product is provided, including computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned method.

[0017] An embodiment of the present disclosure provides an image adjustment method, which can obtain an image to be processed. Then, according to a preset first segmentation rule, the image to be processed is divided into multiple sub-images to be processed. Next, for each preset gray level among multiple preset gray levels in each sub-image to be processed, the dynamic contrast corresponding to each preset gray level is determined. Then, the statistical features corresponding to each sub-image to be processed are determined, and according to the statistical features corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed, the first brightness value corresponding to each preset gray level in each sub-image to be processed is determined. Next, the gray space distribution features corresponding to each sub-image to be processed are determined, and according to the gray space distribution features corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed, the second brightness value corresponding to each preset gray level in each sub-image to be processed is determined. Finally, according to the second brightness value corresponding to each preset gray level in each sub-image to be processed and the size relationship between each gray level and each preset gray level in each sub-image to be processed, the brightness value corresponding to each gray level in each sub-image to be processed is determined, and according to the brightness value corresponding to each gray level in each sub-image to be processed, the brightness of the pixel points corresponding to each gray level in each sub-image to be processed is adjusted. The disclosed embodiment can adjust the dynamic contrast of the sub-images to be processed through the gray space distribution features and statistical features of the sub-images to be processed, and can reduce the probability of over-processing the image to be processed during the brightness adjustment process. In addition, usually, the statistical features can be determined with fewer line caches than the gray space distribution features. Compared with the technical solution of simultaneously determining the statistical features and gray space distribution features, the disclosed embodiment of the present disclosure can reduce the number of occupied line caches, that is, save the computing resources of the chip.

[0018] Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are included in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the present disclosure and are used to explain the principles of the present disclosure.

[0020] Figure 1 FIG. shows a flowchart of an image adjustment method according to an embodiment of the present disclosure.

[0021] Figure 2 FIG. shows a reference diagram for adjusting the number of pixel points of each preset gray level according to an embodiment of the present disclosure.

[0022] Figure 3 FIG. shows a reference diagram for determining gray space distribution features according to an embodiment of the present disclosure.

[0023] Figure 4 Shows a reference schematic diagram of an image adjustment method provided according to an embodiment of the present disclosure.

[0024] Figure 5 Shows a reference schematic diagram of the effect of an image adjustment method provided according to an embodiment of the present disclosure.

[0025] Figure 6 Shows a block diagram of an image adjustment device provided according to an embodiment of the present disclosure.

[0026] Figure 7 Shows a block diagram of an electronic device provided according to an embodiment of the present disclosure. Detailed implementation manners

[0027] The following will describe in detail various exemplary embodiments, features, and aspects of the present disclosure with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0028] In the description of the present disclosure, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present disclosure and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present disclosure.

[0029] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present disclosure, "a plurality" means two or more unless otherwise specifically defined.

[0030] In the present disclosure, unless otherwise clearly specified and limited, the terms "mounted", "connected", "connected to", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0031] In this text, the term "and / or" is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Additionally, the term "at least one" in this text means any one of multiple types or any combination of at least two of multiple types. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set composed of A, B, and C.

[0032] In the related art, the dynamic contrast of the image to be processed is usually adjusted by the histogram equalization algorithm. When the image to be processed contains obvious bright areas or dark areas, it usually causes the bright areas to become brighter after the dark areas are brightened, or the dark areas to become darker after the bright areas are darkened. In addition, when the image to be processed is an image with a narrow gray-scale distribution, such as an image of blue sky and white clouds or an image of a desert, these images will be forcibly stretched, resulting in over-processing phenomena such as "faults" and "halos". If the statistical features and gray-scale spatial distribution features of the local images of each image to be processed are determined, more row caches of the chip will be occupied.

[0033] In view of this, embodiments of the present disclosure provide an image adjustment method, which can obtain an image to be processed. Then, according to a preset first segmentation rule, the image to be processed is divided into a plurality of sub-images to be processed. Next, for each preset gray level among a plurality of preset gray levels in each sub-image to be processed, the dynamic contrast corresponding to each preset gray level is determined. Then, the statistical features corresponding to each sub-image to be processed are determined, and according to the statistical features corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed, the first brightness value corresponding to each preset gray level in each sub-image to be processed is determined. Next, the gray level space distribution features corresponding to each sub-image to be processed are determined, and according to the gray level space distribution features corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed, the second brightness value corresponding to each preset gray level in each sub-image to be processed is determined. Finally, according to the second brightness value corresponding to each preset gray level in each sub-image to be processed and the size relationship between each gray level and each preset gray level in each sub-image to be processed, the brightness value corresponding to each gray level in each sub-image to be processed is determined, and according to the brightness value corresponding to each gray level in each sub-image to be processed, the brightness of the pixel points corresponding to each gray level in each sub-image to be processed is adjusted. The embodiments of the disclosure can adjust the dynamic contrast of the sub-images to be processed through the gray level space distribution features and statistical features of the sub-images to be processed, and can reduce the probability of over-processing the image to be processed during the brightness adjustment process. In addition, usually, the statistical features can be determined with fewer line caches than the gray level space distribution features. Compared with the technical solution of simultaneously determining the statistical features and the gray level space distribution features, the embodiments of the present disclosure can reduce the number of occupied line caches, that is, save the computing resources of the chip.

[0034] Refer to Figure 1 , Figure 1 which shows a flowchart of an image adjustment method according to an embodiment of the present disclosure. As Figure 1 shown, the adjustment method includes:

[0035] Step S100, obtain an image to be processed. Exemplarily, the above image to be processed can be image data that can be processed by any display device in the related art, and the embodiments of the present disclosure do not limit this here.

[0036] Step S200, divide the to-be-processed image into multiple to-be-processed sub-images according to a preset first segmentation rule. Exemplarily, the above first segmentation rule may include: the total number of segments of the to-be-processed image, the number of segments of the to-be-processed image in the horizontal direction, the number of segments of the to-be-processed image in the vertical direction, the size of the to-be-processed sub-image in the horizontal direction, the size of the to-be-processed sub-image in the vertical direction, etc. In one example, the number of pixel points in each row (or the horizontal direction) between the above to-be-processed sub-images is the same, and the number of pixel points in each column (or the vertical direction) is the same. Developers can decide according to the actual situation, and the embodiments of the present disclosure do not limit this here.

[0037] Step S300, for each preset gray level among multiple preset gray levels in each to-be-processed sub-image, determine the dynamic contrast corresponding to each preset gray level. Exemplarily, the above multiple preset gray levels may be sampled gray levels preset by developers among all the gray levels of the to-be-processed image. The above dynamic contrast can be expressed as the DCR (Dynamic Contrast Ratio) value in the related art.

[0038] In a possible implementation manner, step S300 may include: determining the gray level histogram corresponding to the to-be-processed sub-image. Wherein, the gray level histogram is used to represent the number of pixel points in the to-be-processed sub-image distributed on the multiple preset gray levels. Exemplarily, each pixel point may correspond to a different gray level value, which is used to represent the difference of each pixel point in the gray level space. The construction process of the above gray level histogram can refer to the related art, and the embodiments of the present disclosure do not limit this here. Exemplarily, according to different images, the total number of gray levels is also different. For example: the total number of gray levels is 8, the total number of gray levels is 256, the total number of gray levels is 512. The higher the total number of gray levels, the finer the performance effect of the image in the gray level space. The selection process of the multiple preset gray levels is not limited in the embodiments of the present disclosure here. For example, the above multiple preset gray levels can be obtained by evenly dividing the total number of gray levels. Each preset gray level may correspond to a gray level value, and the pixel points in the to-be-processed sub-image with the same gray level value as it can be used as the pixel points corresponding to this preset gray level. Then, adjust the number of pixel points of each preset gray level in the gray level histogram whose number of pixel points is greater than a preset threshold to the preset threshold. As Figure 2 shown, Figure 2 shows a reference schematic diagram for adjusting the number of pixel points of each preset gray level provided by an embodiment of the present disclosure. Figure 2 The abscissa in represents the numbers of multiple preset gray levels, and the ordinate represents the number of pixel points. Exemplarily, if the number of pixel points corresponding to a certain preset gray level is higher than the preset threshold (or Figure 2If the number of pixels in the preset gray level is higher than the preset pixel number threshold, the pixels higher than the preset threshold can be evenly distributed (i.e., balanced in the figure) to other preset gray levels until the number of pixels corresponding to each preset gray level does not exceed the preset threshold. This can enhance the contrast of the sub-image to be processed. Then, according to the adjusted gray level histogram, the cumulative distribution histogram corresponding to the sub-image to be processed is determined. Wherein, the cumulative distribution histogram is used to represent the total number of pixels whose gray level values are less than or equal to each preset gray level. Exemplarily, if Hist(i) represents the total number of pixels corresponding to the i-th preset gray level among the multiple preset gray levels in the gray level histogram, and CDF(i) represents the value in the cumulative distribution histogram corresponding to the i-th preset gray level, then CDF(i) can be expressed as CDF(i) = Hist(i) + Hist(i - 1) + … + Hist(1). Finally, according to the cumulative distribution histogram, the dynamic contrast corresponding to each preset gray level in the sub-image to be processed is determined. Exemplarily, if DCR(i) is used to represent the dynamic contrast corresponding to the i-th preset gray level, then DCR(i) can be expressed as: DCR(i) = (2 databits - 1)*CDF(i) / (Width * Height), where Width and Height respectively represent the width and height of the sub-image to be processed (the unit can be the number of pixels), 2 databits is the total number of gray levels, and databits can take any positive integer.

[0039] Continue to refer to Figure 1 , step S400, determine the statistical features corresponding to each sub-image to be processed, and according to the statistical features corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed, determine the first brightness value corresponding to each preset gray level in each sub-image to be processed. Wherein, the statistical features are used to represent the difference between the dynamic contrasts corresponding to each preset gray level in the same sub-image to be processed. Exemplarily, the above statistical features can be the coefficient of standard deviation or the coefficient of average difference in related technologies, and the embodiments of the present disclosure do not limit this here. In a possible implementation manner, determining the statistical features corresponding to each sub-image to be processed in step S400 may include: determining the average value and the standard deviation of the gray level values corresponding to each preset gray level in each sub-image to be processed, and taking the ratio between the standard deviation and the average value as the statistical features corresponding to each sub-image to be processed. Exemplarily, the above statistical feature Coe_dis can be expressed as: Wherein, σ is used to represent the standard deviation, is the average value of the gray scale values of multiple preset gray scales, and n is the total number of multiple preset gray scales. In this case, the above first luminance value DCR_LUT_1 can be expressed as follows: DCR_LUT_1 = DCR_LUT + (C1 * Coe_dis) * (DCR_LUT - X), where DCR_LUT is the luminance value corresponding to the dynamic contrast of a certain preset gray scale (which can be determined by a look-up table that stores the correspondence between dynamic contrast and luminance and can be set by developers), X is the abscissa value of the preset gray scale in the gray scale histogram, and C1 is a first empirical constant, which can be 1.2.

[0040] Step S500: Determine the gray-scale spatial distribution feature corresponding to each sub-image to be processed, and determine the second brightness value corresponding to each preset gray level in each sub-image to be processed according to the gray-scale spatial distribution feature corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed. In a possible implementation manner, determining the gray-scale spatial distribution feature corresponding to each sub-image to be processed in step S500 may include: dividing each sub-image to be processed into multiple regional images according to a preset second segmentation rule. Exemplarily, the size of each regional image may be equal or unequal, and the embodiments of the present disclosure do not limit this here. The embodiments of the present disclosure can further save the computing resources of the chip by further dividing the sub-image to be processed into regional images. Then, for each regional image corresponding to each sub-image to be processed, perform at least two cropping operations on each regional image corresponding to each sub-image to be processed according to a preset third segmentation rule to obtain at least one set of cropped images for each regional image corresponding to each sub-image to be processed. Wherein, each set of cropped images includes two cropped images, and the number of pixel points in each row and each column of each cropped image in the same set of cropped images is equal. Then determine the gray-scale difference between the two cropped images in each set of cropped images for each regional image corresponding to each sub-image to be processed. Exemplarily, the above gray-scale difference may be expressed as the sum of the absolute values of the gray-scale value differences. Then, according to the gray-scale difference between the two cropped images in each set of cropped images for each regional image corresponding to each sub-image to be processed, determine the gray-scale spatial distribution feature of each regional image corresponding to each sub-image to be processed. Exemplarily, the sum of the gray-scale differences of multiple sets of cropped images corresponding to each above-mentioned regional image may be added to obtain a sum value or the average value of the sum value, or the sum value or the average value of the sum value may be obtained by weighted summation according to the number of pixel points in each set of cropped images, and the sum value or the average value is used as the gray-scale spatial distribution feature of each above-mentioned regional image. Finally, perform feature fusion on the gray-scale spatial distribution features of each regional image corresponding to each sub-image to be processed to obtain the gray-scale spatial distribution feature corresponding to each sub-image to be processed. Exemplarily, the sum of the gray-scale spatial distribution features of multiple regional images corresponding to each sub-image to be processed may be added to obtain a sum value or the average value of the sum value, or the sum value or the average value of the sum value may be obtained by weighted summation according to the number of pixel points in each regional image, and the sum value or the average value is used as the gray-scale distribution feature corresponding to each above-mentioned sub-image to be processed.

[0041] Combined with Figure 3 shown in Figure 3 FIG. shows a reference schematic diagram for determining the gray-scale spatial distribution feature according to an embodiment of the present disclosure. Figure 3Taking an image with a size of 4 pixels * 5 pixels of the sub-image to be processed as an example, each square represents a pixel, and the number in the square represents the gray value corresponding to the pixel. The white squares in the first image to the sixth image represent the cropped pixel points. In a possible implementation manner, the at least two croppings of each regional image corresponding to each sub-image to be processed according to the preset third segmentation rule to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed may include: cropping the pixel points of the first preset number of rows from the first row to the last row of each regional image corresponding to each sub-image to be processed to obtain the first image of each regional image corresponding to each sub-image to be processed. Cropping the pixel points of the first preset number of rows from the last row to the first row of each regional image corresponding to each sub-image to be processed to obtain the second image of each regional image corresponding to each sub-image to be processed. Taking the first image and the second image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed. Exemplarily, the specific value of the above first preset number of rows is not limited in the embodiments of the present disclosure, and developers can set it according to actual needs. In a possible implementation manner, the at least two croppings of each regional image corresponding to each sub-image to be processed according to the preset third segmentation rule to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed may include: cropping the pixel points of the first preset number of columns from the first column to the last column of each regional image corresponding to each sub-image to be processed to obtain the third image of each regional image corresponding to each sub-image to be processed. Cropping the pixel points of the first preset number of columns from the last column to the first column of each regional image corresponding to each sub-image to be processed to obtain the fourth image of each regional image corresponding to each sub-image to be processed. Taking the third image and the fourth image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed. Exemplarily, the specific value of the above first preset number of columns is not limited in the embodiments of the present disclosure, and developers can set it according to actual needs. In a possible implementation manner, the at least two croppings of each regional image corresponding to each sub-image to be processed according to the preset third segmentation rule to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed may include: cropping the pixel points of the second preset number of rows from the first row to the last row and the second preset number of columns from the first column to the last column of each regional image corresponding to each sub-image to be processed to obtain the fifth image of each regional image corresponding to each sub-image to be processed.For each region image corresponding to each sub-image to be processed, crop the pixel points of the second preset number of rows from the last row to the first row and the second preset number of columns from the last column to the first column, to obtain a sixth image of each region image corresponding to each sub-image to be processed. Use the fifth image and the sixth image of each region image corresponding to each sub-image to be processed as a set of cropped images of each region image corresponding to each sub-image to be processed. Exemplarily, the specific values of the above-mentioned second preset number of rows and second preset number of columns are not limited in the embodiments of the present disclosure, and can be set by developers according to actual needs.

[0042] Taking the first column pixels of the first image and the second image as an example, the gray values of the first column pixels of the first image are 1, 1, 5, 6, 8 respectively, and the gray values of the first column pixels of the second image are 2, 3, 5, 7, 1 respectively. Then the gray difference between the first column pixels of the first image and the second image is |1 - 2|, |1 - 3|, |5 - 5|, |6 - 7|, |8 - 1|, that is, 1, 2, 0, 1, 7. The sum of the gray differences in the first column is 11. Calculate the gray differences of all pixel points accordingly. If the gray space distribution feature of the region image is Coe_spa_1, then Coe_spa_1 can be expressed by the following formula: Among them, Sum_Diff_x is used to represent the sum of gray differences between the first image and the second image, Size_x is used to represent the number of pixel points in one row of the first image and the second image, Summ_Diff_y is used to represent the sum of gray differences between the third image and the fourth image, Size_y is used to represent the number of pixel points in one column of the third image and the fourth image, Summ_Diff_xy is used to represent the sum of gray differences between the fifth image and the sixth image, and Size_xy is used to represent the sum of the number of pixel points in one row and one column of the fifth image or the sixth image minus the number of overlapping pixel points (in this example, the two overlap one pixel point, so subtract 1). Continuing with the above example, Sum_Diff_x is 38, Size_x is 5, Summ_Diff_y is 39, Size_y is 4, Summ_Diff_xy is 43, and Size_xy is 8. Then, according to the methods of summing, averaging, and weighted averaging in the above text, determine Coe_spa corresponding to each sub-image to be processed through Coe_spa_1 corresponding to each group of region images. Then the second brightness value DCR_LUT_2 can be expressed in the following way: DCR_LUT_2 = DCR_LUT_1 + (1 + C2 * Coe_spa) * (DCR_LUT_1 - X). Among them, C2 is the second empirical constant, and the value of C2 can be any value from 0 to 10, and can be specifically set by developers according to actual situations.

[0043] Continue to refer to Figure 1As shown in the figure, in step S600, according to the second luminance values corresponding to each preset gray level in each sub-image to be processed, and the magnitude relationship between each gray level and each preset gray level in each sub-image to be processed, determine the luminance value corresponding to each gray level in each sub-image to be processed, and adjust the luminance of the pixel points corresponding to each gray level in each sub-image to be processed according to the luminance value corresponding to each gray level in each sub-image to be processed. In one example, since what is determined is the luminance corresponding to each preset gray level, and the preset gray levels are artificially set sampling gray levels, the luminance values of the pixel points corresponding to each gray level in the sub-image to be processed can be determined by means of an interpolation algorithm in related technologies according to the magnitude order of the gray levels. This is not elaborated in the embodiments of the present disclosure.

[0044] Referring to Figure 4 as shown in the figure, Figure 4 FIG. shows a reference schematic diagram of an image adjustment method according to an embodiment of the present disclosure. Combining Figure 4 as shown in the figure, the image to be processed can be divided into 8×4 sub-images to be processed. Among them, taking the four right sub-images to be processed as an example, the abscissa in the coordinate system in the figure represents the gray level value, and the ordinate represents the dynamic contrast ratio. The relatively white curve in the coordinate system represents the original dynamic contrast ratio curve (since the dynamic contrast ratio is related to luminance, it can also be called the luminance curve), and the relatively black curve represents the dynamic contrast ratio curve adjusted by the adjustment method provided by the embodiment of the present disclosure. It can be seen that the adjustment method provided by the embodiment of the present disclosure can perform local luminance adjustment according to different sub-images to be processed, thereby reducing the probability of the situation where the bright area becomes brighter or the dark area becomes darker during overall adjustment. Referring to Figure 5 as shown in the figure, Figure 5 FIG. shows a reference schematic diagram of the effect of an image adjustment method according to an embodiment of the present disclosure. The left image in the figure is the image before adjustment, and the right image is the image after adjustment. It can be seen that the embodiments of the present disclosure can improve the clarity of the image in areas with a narrow gray level distribution.

[0045] Referring to Figure 6 as shown in the figure, Figure 6 FIG. shows a block diagram of an image adjustment device according to an embodiment of the present disclosure. Combining Figure 6, the adjustment device 100 includes: an image acquisition module 110 for acquiring an image to be processed; an image division module 120 for dividing the image to be processed into a plurality of sub-images to be processed according to a preset first segmentation rule; a dynamic contrast determination module 130 for determining, for each preset gray level among a plurality of preset gray levels in each sub-image to be processed, the dynamic contrast corresponding to each preset gray level; a first brightness value determination module 140 for determining the statistical feature corresponding to each sub-image to be processed, and determining, according to the statistical feature corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed, the first brightness value corresponding to each preset gray level in each sub-image to be processed; wherein the statistical feature is used to represent the difference between the dynamic contrasts corresponding to each preset gray level in the same sub-image to be processed; a second brightness value determination module 150 for determining the gray level space distribution feature corresponding to each sub-image to be processed, and determining, according to the gray level space distribution feature corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed, the second brightness value corresponding to each preset gray level in each sub-image to be processed; a brightness adjustment module 160 for determining the brightness value corresponding to each gray level in each sub-image to be processed according to the second brightness value corresponding to each preset gray level in each sub-image to be processed and the magnitude relationship between each gray level and each preset gray level in each sub-image to be processed, and adjusting the brightness of the pixel points corresponding to each gray level in each sub-image to be processed according to the brightness value corresponding to each gray level in each sub-image to be processed.

[0046] In a possible implementation manner, determining the gray level space distribution feature corresponding to each sub-image to be processed includes: dividing each sub-image to be processed into a plurality of regional images according to a preset second segmentation rule; for each regional image corresponding to each sub-image to be processed, performing at least two croppings on each regional image corresponding to each sub-image to be processed according to a preset third segmentation rule to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed; wherein each set of cropped images includes two cropped images, and the number of pixel points in each row and each column of each cropped image in the same set of cropped images is equal; determining the gray level difference between the two cropped images in each set of cropped images of each regional image corresponding to each sub-image to be processed; determining the gray level space distribution feature of each regional image corresponding to each sub-image to be processed according to the gray level difference between the two cropped images in each set of cropped images of each regional image corresponding to each sub-image to be processed; and performing feature fusion on the gray level space distribution features of each regional image corresponding to each sub-image to be processed to obtain the gray level space distribution feature corresponding to each sub-image to be processed.

[0047] In a possible implementation manner, according to a preset third segmentation rule, each regional image corresponding to each sub-image to be processed is cropped at least twice to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed, including at least one of the following: cropping pixel points of a first preset number of rows from the first row to the last row of each regional image corresponding to each sub-image to be processed to obtain a first image of each regional image corresponding to each sub-image to be processed; cropping pixel points of the first preset number of rows from the last row to the first row of each regional image corresponding to each sub-image to be processed to obtain a second image of each regional image corresponding to each sub-image to be processed; using the first image and the second image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed; cropping pixel points of a first preset number of columns from the first column to the last column of each regional image corresponding to each sub-image to be processed to obtain a third image of each regional image corresponding to each sub-image to be processed; cropping pixel points of the first preset number of columns from the last column to the first column of each regional image corresponding to each sub-image to be processed to obtain a fourth image of each regional image corresponding to each sub-image to be processed; using the third image and the fourth image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed; cropping pixel points of a second preset number of rows from the first row to the last row and a second preset number of columns from the first column to the last column of each regional image corresponding to each sub-image to be processed to obtain a fifth image of each regional image corresponding to each sub-image to be processed; cropping pixel points of the second preset number of rows from the last row to the first row and the second preset number of columns from the last column to the first column of each regional image corresponding to each sub-image to be processed to obtain a sixth image of each regional image corresponding to each sub-image to be processed; using the fifth image and the sixth image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed.

[0048] In a possible implementation manner, determining the statistical feature corresponding to each sub-image to be processed includes: determining the average value and the standard deviation of the gray values corresponding to each preset gray level in each sub-image to be processed, and using the ratio between the standard deviation and the average value as the statistical feature corresponding to each sub-image to be processed.

[0049] In a possible implementation, determining the dynamic contrast corresponding to each preset gray level includes: determining a gray level histogram corresponding to the sub-image to be processed; wherein, the gray level histogram is used to represent the number of pixel points in the sub-image to be processed distributed on the multiple preset gray levels; adjusting the number of pixel points of each preset gray level in the gray level histogram whose number of pixel points is greater than a preset threshold to the preset threshold; determining a cumulative distribution histogram corresponding to the sub-image to be processed according to the adjusted gray level histogram; wherein, the cumulative distribution histogram is used to represent the total number of pixel points whose gray level value is less than or equal to each preset gray level; determining the dynamic contrast corresponding to each preset gray level in the sub-image to be processed according to the cumulative distribution histogram.

[0050] The embodiments of the present disclosure further provide a chip, which is used to execute the image adjustment method described in any one of the above.

[0051] The embodiments of the present disclosure further provide a display device, including a plurality of display units and at least one of the above adjustment devices, or including the chip.

[0052] In a possible implementation, the display unit includes a display panel, and the display panel includes at least one of a liquid crystal display panel, a micro light emitting diode display panel, a light emitting diode display panel, a mini light emitting diode display panel, a quantum dot light emitting diode display panel, an organic light emitting diode display panel, a cathode ray tube display panel, a digital light processing display panel, a field emission display panel, a plasma display panel, an electrophoretic display panel, an electro-wetting display panel, and a small pitch display panel.

[0053] The embodiments of the present disclosure further provide an electronic device, including the display device.

[0054] The embodiments of the present disclosure further provide an image adjustment device, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to implement the above image adjustment method when executing the instructions stored in the memory.

[0055] The embodiments of the present disclosure further provide a non-volatile computer-readable storage medium, on which computer program instructions are stored, and wherein, when the computer program instructions are executed by a processor, the above method is implemented.

[0056] Exemplarily, the electronic device in this embodiment includes, but is not limited to, a desktop computer, a television, a mobile device with a large screen such as a mobile phone, a tablet computer, and other common electronic devices that require multiple chips to be cascaded and connected to achieve driving.

[0057] Exemplarily, the electronic device may also be a user equipment (UE), a mobile device, a user terminal, a terminal, a handheld device, a computing device, or a vehicle-mounted device, etc. Exemplarily, some examples of terminals are: a display, a smart phone or a portable device, a mobile phone, a tablet computer, a laptop computer, a palm computer, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a wireless terminal in vehicle-to-everything, etc. For example, the server may be a local server or a cloud server.

[0058] Figure 7 FIG. shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device. Exemplarily, the above-mentioned electronic device may include the above-mentioned adjustment device or the above-mentioned display device. Referring to Figure 7 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0059] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSD TM or the like.

[0060] In an exemplary embodiment, a non-volatile computer-readable storage medium is further provided, such as a memory 1932 including computer program instructions, and the computer program instructions can be executed by a processing component 1922 of the electronic device 1900 to complete the above method.

[0061] The above description is only a exemplary implementation manner of the present invention, rather than being used to limit the protection scope of the present invention. The protection scope of the present invention is determined by the appended claims.

[0062] The special term "exemplary" herein means "serving as an example, embodiment, or illustration". Any embodiment described as "exemplary" here does not have to be construed as superior to or better than other embodiments.

[0063] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including a..." does not exclude the presence of additional identical elements in the process, method, article, or device including the element.

[0064] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to multiple embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of an instruction, and the module, the segment of a program, or the part of an instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0065] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to technologies in the market, or to enable other ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An image adjustment method, characterized in that, The adjustment method includes: Obtain the image to be processed; Divide the image to be processed into multiple sub-images to be processed according to a preset first segmentation rule; For each preset gray level among multiple preset gray levels in each sub-image to be processed, determine the dynamic contrast corresponding to each preset gray level; the determination of the dynamic contrast corresponding to each preset gray level includes: determining the gray level histogram corresponding to the sub-image to be processed; wherein, the gray level histogram is used to represent the number of pixel points in the sub-image to be processed distributed on the multiple preset gray levels; Adjust the number of pixel points of each preset gray level in the gray level histogram whose number of pixel points is greater than a preset threshold to the preset threshold; wherein, if the number of pixel points corresponding to a certain preset gray level is higher than the preset threshold, the pixel points exceeding the preset threshold are evenly distributed to other preset gray levels until the number of pixel points corresponding to each preset gray level does not exceed the preset threshold; According to the adjusted gray level histogram, determine the cumulative distribution histogram corresponding to the sub-image to be processed; wherein, the cumulative distribution histogram is used to represent the total number of pixel points whose gray level values are less than or equal to each preset gray level; According to the cumulative distribution histogram, determine the dynamic contrast corresponding to each preset gray level in the sub-image to be processed; Determine the statistical feature corresponding to each sub-image to be processed, and according to the statistical feature corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed, determine the first brightness value corresponding to each preset gray level in each sub-image to be processed; wherein, the statistical feature is used to represent the difference between the dynamic contrasts corresponding to each preset gray level in the same sub-image to be processed; Determine the gray level spatial distribution feature corresponding to each sub-image to be processed, and according to the gray level spatial distribution feature corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed, determine the second brightness value corresponding to each preset gray level in each sub-image to be processed; According to the second brightness value corresponding to each preset gray level in each sub-image to be processed and the size relationship between each gray level and each preset gray level in each sub-image to be processed, determine the brightness value corresponding to each gray level in each sub-image to be processed, and according to the brightness value corresponding to each gray level in each sub-image to be processed, adjust the brightness of the pixel points corresponding to each gray level in each sub-image to be processed.

2. The adjustment method according to claim 1, characterized in that, The determination of the gray level spatial distribution feature corresponding to each sub-image to be processed includes: Divide each sub-image to be processed into multiple regional images according to a preset second segmentation rule; For each regional image corresponding to each sub-image to be processed, at least two croppings are performed on each regional image corresponding to each sub-image to be processed according to a preset third segmentation rule, so as to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed; wherein, each set of cropped images includes two cropped images, and the number of pixel points in each row and each column between each pair of cropped images in the same set of cropped images is equal; Determine the gray difference between the two cropped images in each set of cropped images of each regional image corresponding to each sub-image to be processed; Determine the gray space distribution characteristics of each regional image corresponding to each sub-image to be processed according to the gray difference between the two cropped images in each set of cropped images of each regional image corresponding to each sub-image to be processed; Perform feature fusion on the gray space distribution characteristics of each regional image corresponding to each sub-image to be processed to obtain the gray space distribution characteristics corresponding to each sub-image to be processed.

3. The adjustment method according to claim 2, wherein The step of performing at least two croppings on each regional image corresponding to each sub-image to be processed according to a preset third segmentation rule to obtain at least one set of cropped images of each regional image corresponding to each sub-image to be processed includes at least one of the following: Crop the pixel points of the first preset number of rows from the first row to the last row of each regional image corresponding to each sub-image to be processed to obtain the first image of each regional image corresponding to each sub-image to be processed; Crop the pixel points of the first preset number of rows from the last row to the first row of each regional image corresponding to each sub-image to be processed to obtain the second image of each regional image corresponding to each sub-image to be processed; Use the first image and the second image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed; Crop the pixel points of the first preset number of columns from the first column to the last column of each regional image corresponding to each sub-image to be processed to obtain the third image of each regional image corresponding to each sub-image to be processed; Crop the pixel points of the first preset number of columns from the last column to the first column of each regional image corresponding to each sub-image to be processed to obtain the fourth image of each regional image corresponding to each sub-image to be processed; use the third image and the fourth image of each regional image corresponding to each sub-image to be processed as a set of cropped images of each regional image corresponding to each sub-image to be processed; For each region image corresponding to each sub-image to be processed, crop the pixel points of the second preset number of rows from the first row to the last row and the second preset number of columns from the first column to the last column to obtain a fifth image of each region image corresponding to each sub-image to be processed; for each region image corresponding to each sub-image to be processed, crop the pixel points of the second preset number of rows from the last row to the first row and the second preset number of columns from the last column to the first column to obtain a sixth image of each region image corresponding to each sub-image to be processed. Use the fifth image and the sixth image of each region image corresponding to each sub-image to be processed as a set of cropped images of each region image corresponding to each sub-image to be processed.

4. The adjustment method according to claim 1, characterized in that The determining the statistical features corresponding to each sub-image to be processed includes: Determine the average value and the standard deviation of the gray values corresponding to each preset gray level in each sub-image to be processed, and use the ratio between the standard deviation and the average value as the statistical feature corresponding to each sub-image to be processed.

5. An image adjustment device, characterized in that, The adjustment device includes: An image acquisition module for acquiring an image to be processed; An image division module for dividing the image to be processed into a plurality of sub-images to be processed according to a preset first segmentation rule; A dynamic contrast determination module for determining the dynamic contrast corresponding to each preset gray level among a plurality of preset gray levels in each sub-image to be processed; the determining the dynamic contrast corresponding to each preset gray level includes: determining the gray level histogram corresponding to the sub-image to be processed; wherein, the gray level histogram is used to represent the number of pixel points in the sub-image to be processed distributed on the plurality of preset gray levels. Adjust the number of pixel points of each preset gray level in the gray level histogram where the number of pixel points is greater than a preset threshold to the preset threshold; wherein, if the number of pixel points corresponding to a certain preset gray level is higher than the preset threshold, evenly distribute the pixel points exceeding the preset threshold to other preset gray levels until the number of pixel points corresponding to each preset gray level does not exceed the preset threshold. According to the adjusted gray level histogram, determine the cumulative distribution histogram corresponding to the sub-image to be processed; wherein, the cumulative distribution histogram is used to represent the total number of pixel points whose gray level values are less than or equal to each preset gray level. According to the cumulative distribution histogram, determine the dynamic contrast corresponding to each preset gray level in the sub-image to be processed. A first brightness value determination module for determining the statistical features corresponding to each sub-image to be processed, and determining the first brightness value corresponding to each preset gray level in each sub-image to be processed according to the statistical features corresponding to each sub-image to be processed and the dynamic contrast corresponding to each preset gray level in each sub-image to be processed; wherein, the statistical features are used to represent the difference between the dynamic contrasts corresponding to each preset gray level in the same sub-image to be processed. A second brightness value determination module is configured to determine the grayscale spatial distribution characteristics corresponding to each sub-image to be processed, and determine the second brightness value corresponding to each preset gray level in each sub-image to be processed according to the grayscale spatial distribution characteristics corresponding to each sub-image to be processed and the first brightness value corresponding to each preset gray level in each sub-image to be processed; A brightness adjustment module is configured to determine the brightness value corresponding to each gray level in each sub-image to be processed according to the second brightness value corresponding to each preset gray level in each sub-image to be processed and the magnitude relationship between each gray level and each preset gray level in each sub-image to be processed, and adjust the brightness of the pixel points corresponding to each gray level in each sub-image to be processed according to the brightness value corresponding to each gray level in each sub-image to be processed.

6. A chip, characterized in that, The chip is used to execute the image adjustment method according to any one of claims 1 to 4.

7. A display device, characterized in that, It includes a plurality of display units and at least one image adjustment device according to claim 5, or includes at least one chip according to claim 6.

8. The display device according to claim 7, wherein The display unit includes a display panel, and the display panel includes at least one of a liquid crystal display panel, a micro light-emitting diode display panel, a light-emitting diode display panel, a mini light-emitting diode display panel, a quantum dot light-emitting diode display panel, an organic light-emitting diode display panel, a cathode ray tube display panel, a digital light processing display panel, a field emission display panel, a plasma display panel, an electrophoretic display panel, an electro-wetting display panel, and a small pitch display panel.

9. An electronic device, characterized in that, It includes a display device according to claim 7 or 8.

Images