Backlight brightness determination method and device, display device and storage medium

Abstract

The invention provides a backlight brightness determination method and device, a display device and a storage medium, and relates to the technical field of display. The backlight brightness determination method comprises the following steps: dividing an image to be displayed into a plurality of sub-images; determining respective first gray levels of the plurality of sub-images based on gray level differences between respective foreground areas and background areas of the plurality of sub-images; determining respective second gray levels of the plurality of sub-images within a specified range based on respective gray level distribution of the foreground region and the background region, the specified range being determined based on the first gray level; and determining a backlight brightness value for each of the plurality of sub-images based on the first gray level and the second gray level of each of the sub-images.

Description

Technical Field

[0001] The present disclosure relates to the field of display technologies, and in particular, to a method for determining backlight brightness, a determining device, a display device, and a storage medium. Background Art

[0002] In the field of liquid crystal display (LCD), the regional dimming technology can achieve local brightness control by adjusting the backlight brightness in partitions. The display quality of an LCD is closely related to the magnitude of the backlight brightness value in the backlight module. If the backlight brightness value of a certain area cannot match the image in that area, it may lead to a reduction in the image display quality. Summary of the Invention

[0003] The present disclosure provides a method for determining backlight brightness, a determining device, a display device, and a storage medium.

[0004] According to a first aspect, the present disclosure provides a method for determining backlight brightness, including: dividing an image to be displayed into a plurality of sub-images; determining a first gray level of each of the plurality of sub-images based on a gray level difference between a foreground region and a background region of each of the plurality of sub-images; determining a second gray level of each of the plurality of sub-images within a specified range based on gray level distributions of the foreground region and the background region, the specified range being determined based on the first gray level; and determining a backlight brightness value for each of the plurality of sub-images based on the first gray level and the second gray level of each sub-image.

[0005] According to a second aspect, the present disclosure provides a device for determining backlight brightness, including: a dividing module configured to divide an image to be displayed into a plurality of sub-images; a first determining module configured to determine a first gray level of each of the plurality of sub-images based on a gray level difference between a foreground region and a background region of each of the plurality of sub-images; a second determining module configured to determine a second gray level of each of the plurality of sub-images within a specified range based on gray level distributions of the foreground region and the background region, the specified range being determined based on the first gray level; and a third determining module configured to determine a backlight brightness value for each of the plurality of sub-images based on the first gray level and the second gray level of each sub-image.

[0006] According to a third aspect, the present disclosure provides a display device, including: a backlight module; and the device for determining backlight brightness provided by the present disclosure, electrically connected to the backlight module and configured to output a backlight driving signal to the backlight module, the backlight driving signal being determined by the backlight brightness value.

[0007] According to a fourth aspect, the present disclosure provides a display device, including: a backlight module; and a processor configured to execute the method for determining backlight brightness provided by the present disclosure and output the backlight brightness value to the backlight module.

[0008] According to a fifth aspect, the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are for causing a computer to execute the backlight brightness determination method provided by the present disclosure.

[0009] According to a sixth aspect, the present disclosure provides a computer program product including computer programs / instructions, wherein when the computer programs / instructions are executed by a processor, the backlight brightness determination method provided by the present disclosure is implemented. Description of the Drawings

[0010] Figure 1 is a flowchart of the backlight brightness determination method according to an embodiment of the present disclosure;

[0011] Figure 2 is a schematic structural diagram of a backlight source according to an embodiment of the present disclosure;

[0012] Figure 3 is a schematic diagram of the gray-scale distribution of a sub-image according to an embodiment of the present disclosure;

[0013] Figure 4 is a schematic diagram of determining a backlight brightness value according to an embodiment of the present disclosure;

[0014] Figure 5 is a schematic diagram of a device for determining compensation data according to an embodiment of the present disclosure;

[0015] Figure 6 is a schematic diagram of a display device according to an embodiment of the present disclosure;

[0016] Figure 7 is a schematic diagram of a display device according to another embodiment of the present disclosure; and

[0017] Figure 8 is a schematic block diagram of an electronic device according to an embodiment of the present disclosure. Detailed Embodiments

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are only a part rather than all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present disclosure without creative efforts fall within the scope of protection of the present disclosure. It should be noted that throughout the drawings, the same elements are denoted by the same or similar reference numerals. In the following description, some specific embodiments are for illustrative purposes only and should not be construed as any limitation to the present disclosure, but merely as examples of the embodiments of the present disclosure. When it may cause confusion in the understanding of the present disclosure, the conventional structures or configurations will be omitted. It should be noted that the shapes and sizes of the components in the drawings do not reflect the actual sizes and proportions, but only illustrate the content of the embodiments of the present disclosure.

[0019] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the ordinary meanings understood by those skilled in the art. The "first", "second", and similar terms used in the embodiments of the present disclosure do not denote any order, quantity, or importance, but are only used to distinguish different components.

[0020] In addition, in the description of the embodiments of the present disclosure, the term "connected" or "connected to" may mean that two components are directly connected, or may mean that two components are connected via one or more other components. In addition, these two components may be connected or coupled by wired or wireless means.

[0021] In the field of LCDs, the local dimming technology can achieve local brightness control by adjusting the backlight brightness in zones. For example, in the backlight module of an LCD display device, the backlight source is divided into multiple backlight zones, and each zone is an independent backlight source, or the backlight source of each zone has an independent backlight control unit. Through the local dimming technology, it is possible to control each zone to independently adjust the backlight brightness and accurately adjust the brightness and darkness of the backlight within the zone in real time according to the content of the display screen.

[0022] In the local dimming technology, it is crucial to use a suitable backlight brightness determination method according to the content of the image to be displayed to determine the backlight brightness value. The display quality of the LCD is closely related to the magnitude of the backlight brightness value in the backlight module.

[0023] For example, if the backlight brightness value of the image partition in a low-brightness image is too high, the backlight brightness emitted by the backlight module will be too high, which will further cause problems such as light leakage and high energy consumption. If the backlight brightness value of the image partition in a high-brightness image is too low, the backlight brightness emitted by the backlight module will be too low, which will further cause problems such as image distortion. Therefore, selecting a suitable backlight brightness value has a significant promoting effect on improving the image display quality and reducing energy consumption.

[0024] The backlight brightness determination method in the related art cannot determine an appropriate backlight brightness value according to the content of the image to be displayed, resulting in a reduction in the image display quality.

[0025] Figure 1 It is a flowchart of the backlight brightness determination method according to an embodiment of the present disclosure.

[0026] As Figure 1 shown, the backlight brightness determination method according to an embodiment of the present disclosure may include the following steps S110 to S140. It should be noted that the serial numbers of the respective steps in the following method are only used as representations of the steps for description, and should not be regarded as indicating the execution order of the respective steps. Unless explicitly stated, the method does not need to be executed exactly in the order shown.

[0027] In step S110, the image to be displayed is divided into a plurality of sub-images.

[0028] In an embodiment of the present disclosure, the image to be displayed is the original image data to be displayed on an LCD display device. The LCD display device includes a backlight module. After determining the backlight brightness value through the backlight brightness determination method of the embodiment of the present disclosure, the backlight source of the backlight module emits backlight with a corresponding brightness according to the determined backlight brightness value. After the backlight emitted by the backlight source of the backlight module passes through the polarizer, liquid crystal layer, and filter of the LCD display device, the LCD display device displays the image to be displayed.

[0029] In an embodiment of the present disclosure, the backlight module includes a plurality of backlight zones. Since the emission brightness of the plurality of backlight zones of the backlight module can be independently controlled, therefore, dividing the image to be displayed into a plurality of sub-images can be based on the backlight zones to divide the image to be displayed. For example, the plurality of backlight zones of the backlight module may be arranged in an array. According to the arrangement manner of the plurality of backlight zones, the image to be displayed is divided into a plurality of sub-images, and the sizes of the plurality of sub-images are the same. After obtaining the plurality of sub-images by division, the display brightness of a single sub-image can be adjusted by controlling the emission brightness of a single backlight zone.

[0030] In step S120, based on the gray-scale difference between the foreground region and the background region of each of the plurality of sub-images, the first gray-scale of each of the plurality of sub-images is determined.

[0031] In an embodiment of the present disclosure, before determining the first gray-scale of the sub-image, it is necessary to first convert the sub-image into a gray-scale image, and the gray-scale value quantization range is 0 - 255. The method for converting the sub-image into a gray-scale image may include, but is not limited to, the maximum value principle, the average value method, the weighted value method, and the minimum value method.

[0032] In the embodiments of the present disclosure, after converting the sub-image into a grayscale image, the sub-image is divided into a foreground region and a background region according to the grayscale characteristics of the sub-image. For example, the sub-image can be divided by a preset segmentation threshold, and the pixels with grayscale less than or equal to the preset segmentation threshold are divided into the background region, and the pixels with grayscale greater than the preset segmentation threshold are divided into the foreground region.

[0033] In the embodiments of the present disclosure, based on the grayscale difference between the foreground region and the background region of each sub-image among multiple sub-images, the first grayscale of each sub-image is determined. The division of the foreground region and the background region of the sub-image can be changed by adjusting the segmentation threshold. When the grayscale difference between the foreground region and the background region of each sub-image is the largest, the segmentation threshold for dividing the foreground region and the background region of the sub-image at this time can be determined as the first grayscale. Determining the first grayscale by maximizing the grayscale difference between the foreground region and the background region can effectively distinguish the bright and dark regions of the sub-image.

[0034] For example, the average grayscale values of the foreground region and the background region can be calculated respectively, and by comparing the difference between the average grayscale value of the foreground region and the average grayscale value of the background region, the grayscale difference between the foreground region and the background region of each sub-image is determined. A larger difference in the average grayscale value indicates a larger grayscale difference between the foreground region and the background region.

[0035] For example, the first grayscale can be determined by the histogram peak-valley method. Based on the grayscale histogram of the grayscale image of the sub-image, the peaks and valleys of the grayscale histogram can be analyzed, and the valley bottom between the two peaks of the histogram is determined as the first grayscale.

[0036] For example, the first grayscale can be determined by the method of fuzzy C-means clustering. The pixel grayscale values of the sub-image can be divided into two fuzzy sets of foreground and background. According to the membership degree, the clustering centers are calculated, and the membership degree and the clustering centers are optimized by minimizing the objective function until the clustering centers are stable. The midpoint between the two clustering centers is determined as the first grayscale.

[0037] In step S130, based on the grayscale distributions of the foreground region and the background region respectively, multiple second grayscales of the respective sub-images are determined within a specified range, and the specified range is determined based on the first grayscale.

[0038] In the embodiments of the present disclosure, the specified range is determined based on the first grayscale. For example, the middle value of the specified range can be the first grayscale, that is, the average value of the minimum grayscale and the maximum grayscale in the specified range is the first grayscale. Performing secondary optimization within the range defined by the first grayscale to determine the second grayscale can avoid wasting resources in global calculations and can also correct possible deviations in the first grayscale.

[0039] In an embodiment of the present disclosure, based on the gray-scale distributions of the foreground region and the background region, the image information included in the foreground region and the background region can be determined. By adjusting the segmentation threshold within a specified range, the division of the foreground region and the background region in the sub-image can be adjusted. When the image information included in the foreground region and the background region is the most, the segmentation threshold at this time can be determined as the second gray-scale.

[0040] For example, the image information included in the foreground region and the background region can be determined by calculating the entropy values of the foreground region and the background region in the sub-image respectively. The higher the entropy value, the more image information. The segmentation threshold when the sum of the entropy values of the foreground region and the background region is the largest is determined as the second gray-scale.

[0041] For example, the image information included in the foreground region and the background region can be determined through frequency-domain analysis. The sub-image is transformed to the frequency domain through Fourier transform. The high-frequency components represent detailed information, and the low-frequency components represent the overall structure. Analyze the proportion of high-frequency energy. If the energy is concentrated in the low frequency, it indicates that the amount of image information is low. If the energy is concentrated in the high frequency, it indicates that the amount of image information is high.

[0042] In step S140, based on the first gray-scale and the second gray-scale of each sub-image, the backlight brightness values for the multiple sub-images are determined respectively.

[0043] In an embodiment of the present disclosure, according to the first gray-scale and the second gray-scale of each sub-image, the backlight brightness value of each sub-image is determined. The first gray-scale is determined according to the gray-scale difference between the foreground region and the background region, and the second gray-scale is determined according to the gray-scale distributions of the foreground region and the background region respectively. Therefore, the pixel distribution in the sub-image can be determined according to the first gray-scale and the second gray-scale.

[0044] For example, the high-brightness region information of each sub-image can be determined. When the high-brightness region is large, the backlight brightness value of the sub-image can be increased. When the high-brightness region is small, the backlight brightness value of the sub-image can be decreased. After determining the backlight brightness value, the light-emitting brightness of the backlight partition corresponding to each sub-image can be controlled through the backlight brightness value of each sub-image.

[0045] Through the embodiments of the present disclosure, based on the gray - scale difference between the foreground region and the background region of the sub - image, the first gray - scale of the sub - image can be determined, which can effectively distinguish the bright and dark regions of the sub - image. Furthermore, it can ensure an obvious difference between the foreground region and the background region, and at the same time delimit a reasonable range for the subsequent determination of the second gray - scale. Based on the gray - scale distributions of the foreground region and the background region respectively, the second gray - scale of the sub - image is determined within a specified range, which can retain the image details of the foreground region and the background region respectively, and can ensure that the gray - scale distributions of the foreground region and the background region are both highly uniform. By solving the segmentation threshold in two directions, and the second segmentation threshold (the second gray - scale) can be considered as an optimization of the first segmentation threshold (the first gray - scale), the accuracy of the determined segmentation threshold can be improved, thereby improving the accuracy of the backlight brightness and achieving precise backlight adjustment.

[0046] For example, under noise interference, the first gray - scale determined according to the gray - scale difference between the foreground region and the background region may misjudge noise as the target, resulting in inaccurate first gray - scale. Determining the second gray - scale within a specified range based on the gray - scale distributions of the foreground region and the background region respectively can suppress such errors and make the obtained second gray - scale closer to the true segmentation point.

[0047] Based on this, for high - brightness images, the determined first gray - scale provides an efficient basic segmentation, which can avoid over - exposure. For low - brightness noisy images, the determined second gray - scale is finely adjusted within a specified range to reduce light leakage. Determining the backlight brightness value of the sub - image based on the first gray - scale and the second gray - scale can achieve a balance between speed and accuracy. The method of this embodiment can significantly improve the accuracy of backlight adjustment in complex image scenarios, reduce power consumption while improving the display quality, and solve the problems of low - brightness light leakage, high - brightness distortion, and noise interference existing in LCD - displayed images in related technologies.

[0048] Figure 2 It is a schematic structural diagram of a backlight source according to an embodiment of the present disclosure.

[0049] As Figure 2 shown, the backlight source BL of the backlight module includes 2 * 4 backlight zones. The backlight source BL includes backlight zone b1 and backlight zone b2. According to the arrangement of multiple backlight zones, the image to be displayed can be divided into 2 * 4 sub - images. The arrangement of multiple sub - images is the same as the arrangement of multiple backlight zones, and multiple sub - images correspond to multiple backlight zones one by one.

[0050] In an embodiment of the present disclosure, based on the sub-images corresponding to the backlight zones b1 and b2 respectively, the backlight brightness values corresponding to the backlight zones b1 and b2 can be determined, and the brightness of the backlight zones b1 and b2 can be adjusted based on the backlight brightness values. By adjusting the emission brightness of a single backlight zone, the display brightness of the sub-image corresponding to the backlight zone can be adjusted.

[0051] Figure 3 It is a schematic diagram of the gray-scale distribution of the sub-image according to an embodiment of the present disclosure.

[0052] As Figure 3 shown, the sub-image P1 includes 3*3 pixels, and the gray-scale of each pixel is different. The sub-image P1 can be the sub-image corresponding to the backlight zone b1 in Figure 2 , that is, the backlight brightness value of the backlight zone b1 can be determined through the sub-image P1, and the backlight brightness of the backlight zone b1 can be adjusted through the backlight brightness value.

[0053] In an embodiment of the present disclosure, gray-scale conversion is performed on multiple sub-images to obtain the gray-scale histograms of the multiple sub-images respectively. Based on the gray-scale distribution characterized by the gray-scale histogram, the multiple gray-scale probabilities of the multiple gray-scales included in each of the multiple sub-images are determined. Based on the multiple gray-scales and the gray-scale probabilities of the multiple gray-scales respectively, the first average gray-scale of the background area and the second average gray-scale of the foreground area are determined. Based on the difference between the first average gray-scale and the second average gray-scale of each of the multiple sub-images, the first gray-scale of each of the multiple sub-images is determined.

[0054] In an embodiment of the present disclosure, gray-scale conversion is performed on each sub-image to obtain the gray-scale image of each sub-image. The specific gray-scale conversion method has been described above and will not be elaborated here. The gray-scale image of each sub-image determines the gray-scale histogram of each sub-image. The gray-scale histogram can be generated by counting the number of pixels at each gray-scale level in each sub-image.

[0055] In an embodiment of the present disclosure, based on the gray-scale distribution characterized by the gray-scale histogram, the multiple gray-scale probabilities of the multiple gray-scales included in each sub-image are determined. The gray-scale probability represents the probability of the gray-scale appearing in the sub-image. For example, if the number of pixels in the sub-image is 100 and the number of pixels with a gray-scale of 45 is 10, then the gray-scale probability of this gray-scale is 0.1. Referring to Figure 3 , if the number of pixels in the sub-image is 9 and the number of pixels with a gray-scale of 255 is 1, then the gray-scale probability of this gray-scale is 1 / 9.

[0056] In an embodiment of the present disclosure, the first average gray-scale represents the average gray-scale of all pixels in the background area, and the second average gray-scale represents the average gray-scale of all pixels in the foreground area. For example, the average gray-scale can be the value obtained by adding the products of each gray-scale in the multiple gray-scales and their corresponding gray-scale probabilities.

[0057] In the embodiments of the present disclosure, based on the difference between the first average gray level and the second average gray level of each sub-image, the first gray level of each sub-image is determined. The difference between the first average gray level and the second average gray level can reflect the gray level difference between the foreground region and the background region. By continuously adjusting the size of the segmentation threshold for dividing the foreground region and the background region, the segmentation threshold when the difference between the first average gray level and the second average gray level is the largest is determined as the first gray level.

[0058] Based on the difference between the first average gray level and the second average gray level of each sub-image, the first gray level of each sub-image can be quickly determined, effectively dividing the bright and dark regions in each sub-image.

[0059] In the embodiments of the present disclosure, the initial first gray level can be determined according to the gray level distribution of the sub-image. For example, the initial first gray level is determined according to the gray level mean or the gray level median of the sub-image, and the sub-image is segmented using the initial first gray level as the segmentation threshold to obtain the background region and the foreground region. The value of the first gray level is adjusted according to the difference between the first average gray level of the background region and the second average gray level of the foreground region, and then the first gray level is determined.

[0060] In some embodiments, based on the gray level probabilities of multiple gray levels respectively, the sum of the gray level probabilities of the foreground region and the sum of the gray level probabilities of the background region are determined. Based on the difference between the gray level probabilities of multiple gray levels in the foreground region and the sum of the gray level probabilities of the foreground region, and the difference between the gray level probabilities of multiple gray levels in the background region and the sum of the gray level probabilities of the background region, the second gray level of each of the multiple sub-images is determined within a specified range.

[0061] In the embodiments of the present disclosure, based on the difference between the gray level probabilities of multiple gray levels in the foreground region and the sum of the gray level probabilities of the foreground region, the gray level distribution of the foreground region can be determined.

[0062] For example, if the gray level probability of a certain gray level in the foreground region is close to the sum of the gray level probabilities of the foreground region, it indicates that the gray level distribution in the foreground region divided according to the first gray level is relatively extreme, there is a relatively steep gray level probability in the foreground region part of the gray level histogram, and the foreground region contains less image information.

[0063] For example, if the gray level probabilities of multiple gray levels in the foreground region are relatively average, it indicates that the gray level distribution in the foreground region divided by the first gray level is relatively average, the curve in the foreground region part of the gray level histogram is relatively flat, and at this time the foreground region contains more image information.

[0064] Similarly, for the background region, the same method can be used to determine the image information contained in the background region. Based on the gray-scale distribution of the foreground region and the background region, the image information contained in the sub-image can be determined. When the image information contained in the sub-image is small, the segmentation threshold can be adjusted within a specified range, and the segmentation threshold when the sub-image contains the most image information is determined as the second gray-scale. Secondary optimization is performed within the range defined by the first gray-scale, which can not only avoid waste of resources in global calculation but also correct possible deviations of the first gray-scale. The first gray-scale can ensure an obvious difference between the foreground region and the background region, and the second gray-scale can ensure a high uniformity in the gray-scale distribution of each of the foreground region and the background region. Determining the second gray-scale within the specified range determined by the first gray-scale can reduce the limitation in a single direction.

[0065] Figure 4 is a schematic diagram of the backlight brightness determination method according to an embodiment of the present disclosure.

[0066] As Figure 4 shown, after dividing the image to be displayed into multiple sub-images, for each sub-image 410, it is necessary to first convert the sub-image 410 to obtain a gray-scale image 420, determine the first gray-scale 430 according to the gray-scale image 420, determine the second gray-scale 440 within the specified range determined based on the first gray-scale 430, determine the target gray-scale 450 based on the first gray-scale 430 and the second gray-scale 440, and finally determine the backlight brightness value 460 according to the target gray-scale 450.

[0067] In the embodiments of the present disclosure, the method for converting a sub-image into a gray-scale image may include, but is not limited to, the maximum principle, the average value method, the weighted value method, and the minimum value method.

[0068] For example, to obtain a gray-scale image using the maximum principle, the sub-image can be converted into a gray-scale image based on formula (1) according to the maximum principle:

[0069] gray(x, y) = max(R(x, y), G(x, y), B(x, y)) (1)

[0070] where (x, y) represents the coordinates of a pixel in the sub-image, R(x, y) represents the red channel component of the pixel with coordinates (x, y) in the sub-image, G(x, y) represents the green channel component of the pixel with coordinates (x, y) in the sub-image, B(x, y) represents the blue channel component of the pixel with coordinates (x, y) in the sub-image. gray(x, y) represents the gray-scale value of the pixel with coordinates (x, y) in the sub-image. By retaining the intensity of the brightest channel, the maximum principle makes the gray-scale image highlight the high-light regions in the original image while ignoring the differences in color itself.

[0071] For example, by using the average value method to obtain a grayscale image, the sub-image can be converted into a grayscale image by the average value method based on formula (2):

[0072] gray(x, y) = (R(x, y) + G(x, y) + B(x, y)) / 3 (2)

[0073] Among them, the meanings of the terms in formula (2) are the same as those in formula (1). The overall brightness of the grayscale image obtained based on the average value method is balanced, but it may cause the image to be grayish.

[0074] In the embodiments of the present disclosure, based on the foreground region and the background region of each sub-image, a first initial grayscale is determined, and the first initial grayscale is the segmentation threshold between the foreground region and the background region. After determining the grayscale image of each sub-image, the number of pixels corresponding to each grayscale in the grayscale image can be statistically analyzed, and a suitable segmentation threshold is selected as the first initial grayscale. Among them, the pixels with a grayscale less than or equal to the first initial grayscale are used as the background region of the sub-image, and the pixels with a grayscale greater than the first initial grayscale are used as the foreground region of the sub-image.

[0075] In the embodiments of the present disclosure, based on the difference between the first average grayscale of the background region and the second average grayscale of the foreground region, the background region and the foreground region are updated so that the difference between the first average grayscale and the second average grayscale increases. The increase in the difference between the first average grayscale and the second average grayscale can maximize the separation degree between the background region and the foreground region, and effectively distinguish the light and dark partitions of the sub-image.

[0076] In the embodiments of the present disclosure, based on the updated background region and the updated foreground region, the first initial grayscale is updated to a first grayscale. The determination of the first grayscale can delimit a reasonable range for subsequent optimization.

[0077] For example, the first grayscale can be determined based on the between-class variance method. The larger the between-class variance between the foreground region and the background region, the greater the difference between the two parts of the foreground region and the background region. For a sub-image with a resolution of A * B (number of pixels), which contains M gray levels {0, 1, 2,..., M - 1}, AB = N 0 +N 1 +N 2 +…+N M-1 Among them, N i is the number of pixels with the gray level i. The gray level probability corresponding to the gray level i

[0078] For example, for a sub-image containing 6 discontinuous gray levels {7, 53, 91, 128, 241, 243}, the gray level of the 0th gray level is 7, and the gray level of the M - 1th gray level is 243.

[0079] If the segmentation threshold is f (the f-th gray level, the first initial gray level), then the pixels with gray levels less than or equal to f are determined as the background region, and the pixels with gray levels greater than f are determined as the foreground region. The total probability of the pixels in the background region can be obtained based on formula (3):

[0080]

[0081] The total probability of the pixels in the foreground region can be obtained based on formula (4):

[0082] P 2 (f) = 1 - P 1 (f) (4)

[0083] The first average gray level of the background region can be obtained based on formula (5):

[0084]

[0085] The second average gray level of the foreground region can be obtained based on formula (6):

[0086]

[0087] The between-class variance C is obtained based on formula (7):

[0088] C(f) = P 1 (f)(μ 1 (f) - μ 0 ) 2 + P 2 (f)(μ 2 (f) - μ 0 ) 2 = P 1 (f)P 2 (f)(μ 1 (f) - μ 2 (f)) 2 (7)

[0089] where μ 0 is the average gray level value of the sub-image. The average gray level value of the sub-image can be obtained by dividing the value obtained by adding the gray level values of all pixels in the sub-image by the number of all pixels in the sub-image.

[0090] Based on formula (8), the segmentation threshold when the between-class variance C is the largest is determined as the first gray level:

[0091]

[0092] where C(G1) is the largest between-class variance and G1 is the first gray level.

[0093] In the embodiments of the present disclosure, by using the between-class variance method to maximize the separation degree between the background region and the foreground region, the first grayscale can be quickly determined, and the bright and dark regions of the sub-image can be effectively distinguished.

[0094] In the embodiments of the present disclosure, based on the foreground region and the background region of each of the multiple sub-images, a second initial grayscale is determined, and the second initial grayscale is the segmentation threshold between the foreground region and the background region. Based on the first grayscale difference between multiple pixels included in the background region and the second grayscale difference between multiple pixels included in the foreground region, the background region and the foreground region are updated so that the first grayscale difference and the second grayscale difference are reduced. The reduction of the first grayscale difference and the second grayscale difference can retain more image information of the background region and the foreground region.

[0095] In the embodiments of the present disclosure, based on the updated background region and the updated foreground region, the second initial grayscale is updated to a second grayscale, and the second grayscale is within a specified range. When the sub-image is a low-contrast image or a noisy image, the second grayscale determined based on the updated background region and the updated foreground region can more accurately segment the background region and the foreground region of the sub-image. The background region and the foreground region segmented based on the second grayscale can retain more image information and image details, reducing the influence of noise and the occurrence of light leakage.

[0096] For example, the second grayscale can be determined by the maximum entropy method.

[0097] In the embodiments of the present disclosure, the specified range can be 90%-110% of the first grayscale. Based on the foreground region and the background region of the sub-image, a second initial grayscale f 2 (the f 2 -th gray level) is determined. Based on the second initial grayscale f 2 , the sub-image is divided into a foreground region and a background region, the gray level region corresponding to the background region is [0, f 2 , and the gray level region corresponding to the foreground region is [f 2 + 1, M - 1].

[0098] The sum of the probabilities of the sub-image gray levels from 0 to f 2 is obtained based on formula (9):

[0099]

[0100] The sum of the probabilities of the sub-image gray levels from f 2 +1 to M - 1 is obtained based on formula (10):

[0101] P e2 (f 2 ) = 1 - P e1 (f 2 ) (10)

[0102] The sum th of the information entropy of the background region and the foreground region total Obtained based on formula (11):

[0103]

[0104] where th(back3) and th(back4) are the information entropy of the background region and the foreground region respectively.

[0105] In the embodiments of the present disclosure, for the flat part of the curve in the sub-image gray histogram, relatively more visual information is obtained, that is, the information entropy is larger. For the steep region in the gray histogram, less visual information is obtained, that is, the information entropy is smaller. By making f 2 Search within the gray range of [0.9G1, 1.1G1], and th total (f 2 ) The gray level G2 corresponding to the maximum value is determined as the second gray level. The maximum entropy method determines the second gray level by maximizing the information entropy, which can retain more image details, especially in complex texture or noise regions (such as faint light spots in a dark scene). The maximum entropy method can more accurately segment the background region and the foreground region in low-contrast or noisy images, avoiding the backlight value deviation caused by traditional methods. Determining the second gray level by performing secondary optimization within the range defined by the first gray level not only avoids the waste of resources in global calculation but also corrects the possible deviation of the first gray level.

[0106] In the embodiments of the present disclosure, based on the first gray level and the second gray level of each sub-image, the target gray level of each sub-image is determined.

[0107] For example, the target gray level can be the average of the first gray level and the second gray level. The target gray level G_final can be obtained based on formula (12):

[0108]

[0109] In the embodiments of the present disclosure, based on the difference between the gray levels of multiple pixels included in each sub-image and their respective target gray levels, the gain coefficient of each sub-image is determined. Based on the target gray level of each sub-image, multiple gain pixels included in each sub-image are determined, and the gray level of the gain pixel is greater than the target gray level. The sub-image is divided into a background region and a foreground region based on the target gray level, and the pixels in the foreground region are multiple gain pixels.

[0110] In the embodiments of the present disclosure, based on the maximum gray level of multiple pixels included in each sub-image, the average gain of multiple gain pixels, and the gain coefficient, the brightness correction amount is determined. Based on the brightness correction amount and the average gain, the backlight brightness value for each sub-image is determined.

[0111] For example, the region where the grayscale value is greater than the target grayscale G_final is called the gain region gain, the pixels within the gain region are gain pixels, and the grayscale mean value of the gain region (the gain average value of multiple gain pixels) is BL avg = mean(gain). The gain coefficient K can be obtained based on formula (13):

[0112]

[0113] where BL max is the maximum grayscale value of the input image. The gain coefficient reflects the information of the high-brightness region in the image partition. When the high-brightness region is large, the gain coefficient is large. When the high-brightness region in the image partition is small and the grayscale value mean of the gain region is close to the target grayscale, the gain coefficient is small at this time.

[0114] The brightness correction amount C or can be obtained based on formula (14):

[0115]

[0116] The backlight brightness value BL of the sub-image new = BL avg + C or . For the backlight brightness value of each sub-image among multiple sub-images, it can be calculated through the above steps.

[0117] In the embodiments of the present disclosure, the first grayscale provides efficient basic segmentation, and the second grayscale makes refined adjustments within a local range. The two achieve a balance between speed and accuracy through mean fusion. The method of this embodiment can significantly improve the accuracy of backlight adjustment in complex image scenarios, reduce power consumption, and improve the display quality at the same time.

[0118] Figure 5 is a schematic diagram of a backlight brightness determination device according to an embodiment of the present disclosure.

[0119] The backlight brightness determination device 500 includes a division module 510, a first determination module 520, a second determination module 530, and a third determination module 540.

[0120] The division module 510 is configured to divide the image to be displayed into multiple sub-images. In one embodiment, the division module 510 can be used to perform the operation S110 described above, which will not be elaborated here.

[0121] The first determination module 520 is configured to determine the first grayscale of each of the multiple sub-images based on the grayscale difference between the foreground region and the background region of each of the multiple sub-images. In one embodiment, the first determination module 520 can be used to perform the operation S120 described above, which will not be elaborated here.

[0122] The second determination module 530 is configured to determine the second grayscale of each of the multiple sub-images within a specified range based on the grayscale distributions of the foreground region and the background region respectively, where the specified range is determined based on the first grayscale. In one embodiment, the second determination module 530 may be used to perform the operation S130 described above, which will not be elaborated here.

[0123] The third determination module 540 is configured to determine the backlight brightness value for each of the multiple sub-images based on the first grayscale and the second grayscale of each of the sub-images. In one embodiment, the third determination module 540 may be used to perform the operation S140 described above, which will not be elaborated here.

[0124] In the embodiment of the present disclosure, the first determination module 520 is used to: determine a first initial grayscale based on the foreground region and the background region of each of the multiple sub-images, where the first initial grayscale is the segmentation threshold between the foreground region and the background region; update the foreground-background region and the background-foreground region based on the difference between the first average grayscale of the foreground-background region and the second average grayscale of the background-foreground region, such that the difference between the first average grayscale and the second average grayscale increases; and update the first initial grayscale to the first grayscale based on the updated foreground-background region and the updated foreground region.

[0125] In the embodiment of the present disclosure, the first determination module 520 is further used to: perform grayscale conversion on the multiple sub-images to obtain the grayscale histogram of each of the multiple sub-images; determine the multiple grayscale probabilities of the multiple grayscales included in each of the multiple sub-images based on the grayscale distribution characterized by the grayscale histogram; determine the first average grayscale of the foreground-background region and the second average grayscale of the background-foreground region based on the multiple grayscales and the multiple grayscale probabilities of each of the multiple grayscales; and determine the first grayscale of each of the multiple sub-images based on the difference between the first average grayscale and the second average grayscale of each of the multiple sub-images.

[0126] In the embodiment of the present disclosure, the second determination module 530 is used to: determine a second initial grayscale based on the foreground region and the background region of each of the multiple sub-images, where the second initial grayscale is the segmentation threshold between the foreground region and the background region; update the foreground-background region and the background-foreground region based on the first grayscale difference between the multiple pixels included in the foreground-background region and the second grayscale difference between the multiple pixels included in the background-foreground region, such that the first grayscale difference and the second grayscale difference decrease; and update the second initial grayscale to the second grayscale within the specified range based on the updated foreground-background region and the updated foreground region.

[0127] In an embodiment of the present disclosure, the second determination module 530 is further configured to: determine the sum of the gray probabilities of the foreground region and the sum of the gray probabilities of the background region based on the gray probabilities of the respective grayscales; and determine the second grayscale of each of the multiple sub-images within a specified range based on the difference between the gray probability of each grayscale in the foreground region and the sum of the gray probabilities of the foreground region, and the difference between the gray probability of each grayscale in the background region and the sum of the gray probabilities of the background region.

[0128] In an embodiment of the present disclosure, the third determination module 540 is configured to: determine the target grayscale of each of the multiple sub-images based on the first grayscale and the second grayscale of each sub-image; determine the gain coefficient of each of the multiple sub-images based on the difference between the grayscale of each of the multiple pixels included in each sub-image and its target grayscale; and determine the backlight brightness value for each of the multiple sub-images based on the gain coefficient of each sub-image.

[0129] In an embodiment of the present disclosure, the third determination module 540 is further configured to: determine multiple gain pixels included in each of the multiple sub-images, where the grayscale of the gain pixels is greater than the target grayscale, based on the target grayscale of each of the multiple sub-images; determine the brightness correction amount based on the maximum grayscale of each of the multiple pixels included in each of the multiple sub-images, the average gain of the multiple gain pixels, and the gain coefficient; and determine the backlight brightness value for each of the multiple sub-images based on the brightness correction amount and the average gain.

[0130] Figure 6 is a schematic diagram of a display device according to an embodiment of the present disclosure.

[0131] The display device 600 includes a backlight module 610 and a backlight brightness determination device 620.

[0132] In an embodiment of the present disclosure, the backlight module 610 is electrically connected to the backlight brightness determination device 620. The backlight brightness determination device 620 may be the backlight brightness determination device 500 described above. The backlight brightness determination device 620 determines a backlight drive signal based on the backlight brightness value, and the backlight module 610 may output backlight of a corresponding brightness based on the backlight drive signal output by the backlight brightness determination device 620.

[0133] Figure 7 is a schematic diagram of a display device according to another embodiment of the present disclosure.

[0134] The display device 700 includes a backlight module 710 and a processor 720.

[0135] In an embodiment of the present disclosure, the processor 720 executes the backlight brightness determination method described above and outputs the backlight brightness value to the backlight module 710.

[0136] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0137] Figure 8 FIG. 4 is a schematic block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0138] As Figure 8 shown, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0139] A plurality of components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0140] The computing unit 801 may be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 executes the backlight brightness determination method described above. For example, in some embodiments, the backlight brightness determination method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the backlight brightness determination method described above may be executed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the backlight brightness determination method by any other suitable means (e.g., by means of firmware).

[0141] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-a-chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor, that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0145] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0146] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs that run on respective computers and have a client-server relationship with each other. Among them, the server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system, or a server combined with a blockchain.

[0147] 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 various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown 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 or flowchart, as well as combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0148] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features recited in the various embodiments and / or claims of the present disclosure can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0149] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present disclosure.

Claims

1. A method for determining backlight brightness, comprising: Dividing the image to be displayed into a plurality of sub-images; Determining a first grayscale of each of the plurality of sub-images based on a grayscale difference between a foreground area and a background area of ​​each of the plurality of sub-images; Based on the grayscale distribution of each of the foreground area and the background area, determining a second grayscale of each of the plurality of sub-images within a specified range, wherein the specified range is determined based on the first grayscale; as well as Based on the first grayscale and the second grayscale of each of the sub-images, a backlight brightness value for each of the plurality of sub-images is determined.

2. The method according to claim 1, wherein: The determining, based on the first grayscale and the second grayscale of each of the sub-images, a backlight brightness value for each of the plurality of sub-images comprises: Determining target grayscales of each of the plurality of sub-images based on the first grayscale and the second grayscale of each of the sub-images; Determining gain coefficients of the respective sub-images based on differences between grayscales of the respective pixels included in the respective sub-images and respective target grayscales; and Based on the gain coefficient of each of the sub-images, a backlight brightness value for each of the plurality of sub-images is determined.

3. The method according to claim 2, wherein: The determining, based on the gain coefficient of each of the sub-images, a backlight brightness value for each of the plurality of sub-images comprises: Based on the target grayscales of the plurality of sub-images, determining a plurality of gain pixels included in each of the plurality of sub-images, wherein the grayscale of the gain pixels is greater than the target grayscale; determining a brightness correction amount based on a maximum grayscale of a plurality of pixels included in each of the plurality of sub-images, a gain average value of a plurality of gain pixels, and a gain coefficient; and Based on the brightness correction amount and the gain average value, a backlight brightness value for each of the plurality of sub-images is determined.

4. The method according to claim 1, wherein: The determining of the first grayscale of each of the plurality of sub-images based on the grayscale difference between the foreground area and the background area of ​​each of the plurality of sub-images comprises: Determine a first initial grayscale based on the foreground area and the background area of ​​each of the plurality of sub-images, wherein the first initial grayscale is a segmentation threshold between the foreground area and the background area; Based on the difference between the first average grayscale of the background area and the second average grayscale of the foreground area, updating the background area and the foreground area so that the difference between the first average grayscale and the second average grayscale increases; and Based on the updated background area and the updated foreground area, the first initial grayscale is updated to the first grayscale.

5. The method according to claim 1, wherein: The determining, based on the grayscale distribution of each of the foreground area and the background area, the second grayscale of each of the plurality of sub-images within a specified range comprises: Based on the foreground area and the background area of ​​each of the plurality of sub-images, determining a second initial grayscale, wherein the second initial grayscale is a segmentation threshold between the foreground area and the background area; Based on a first grayscale difference between a plurality of pixels included in the background area and a second grayscale difference between a plurality of pixels included in the foreground area, updating the background area and the foreground area such that the first grayscale difference and the second grayscale difference decrease; and Based on the updated background area and the updated foreground area, the second initial grayscale is updated to the second grayscale, and the second grayscale is within the specified range.

6. The method according to claim 1, wherein: The determining of the first grayscale of each of the plurality of sub-images based on the grayscale difference between the foreground area and the background area of ​​each of the plurality of sub-images comprises: Performing grayscale conversion on the multiple sub-images to obtain grayscale histograms of the multiple sub-images; Based on the grayscale distribution represented by the grayscale histogram, determining a plurality of grayscale probabilities of a plurality of grayscales included in each of the plurality of sub-images; Determining a first average grayscale of the background area and a second average grayscale of the foreground area based on the plurality of grayscales and the grayscale probabilities of the respective plurality of grayscales; and Based on the difference between the first average grayscale and the second average grayscale of each of the plurality of sub-images, a first grayscale of each of the plurality of sub-images is determined.

7. The method according to claim 6, wherein: The determining, based on the grayscale distribution of each of the foreground area and the background area, the second grayscale of each of the plurality of sub-images within a specified range comprises: Determining a sum of the grayscale probabilities of the foreground area and a sum of the grayscale probabilities of the background area based on the grayscale probabilities of each of the plurality of grayscales; and Based on the difference between the grayscale probabilities of multiple grayscales in the foreground area and the sum of the grayscale probabilities of the foreground area, and the difference between the grayscale probabilities of multiple grayscales in the background area and the sum of the grayscale probabilities of the background area, the second grayscale of each of the multiple sub-images is determined within a specified range.

8. A backlight brightness determination device, comprising: A division module, used for dividing the image to be displayed into a plurality of sub-images; A first determination module, configured to determine a first grayscale of each of the plurality of sub-images based on a grayscale difference between a foreground area and a background area of ​​each of the plurality of sub-images; A second determination module, configured to determine a second grayscale of each of the plurality of sub-images within a specified range based on the grayscale distribution of each of the foreground area and the background area, wherein the specified range is determined based on the first grayscale; as well as The third determination module is configured to determine a backlight brightness value for each of the plurality of sub-images based on the first grayscale and the second grayscale of each of the sub-images.

9. A display device, comprising: Backlight module; as well as The backlight brightness determination device described in claim 8 is electrically connected to the backlight module and is configured to output a backlight driving signal to the backlight module, wherein the backlight driving signal is determined by the backlight brightness value.

10. A display device, comprising: Backlight module; A processor is configured to execute the method according to any one of claims 1 to 7, and output the backlight brightness value to the backlight module.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

12. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images