Brightness mapping method based on Mini LED backlight and backlight system

Abstract

The invention relates to the technical field of display, and discloses a brightness mapping method based on Mini LED backlight and a backlight system.The method comprises the steps that an input image is analyzed, and image features are obtained; according to the image features, adaptively dividing a dimming area; and for each dimming area, realizing conversion from pixel brightness to Mini LED backlight brightness through nonlinear mapping in combination with human visual characteristics. By analyzing the input image to obtain the characteristics, adaptively dividing the dimming area and combining the human eye visual characteristics to carry out nonlinear mapping, the contrast ratio of the display image is effectively improved, the image details are reserved to the maximum extent, the image quality problem caused by dark area detail loss and bright area overexposure is remarkably improved, and the display effect better meets the human eye visual requirements.

Description

Technical Field

[0001] The present invention relates to the field of display technologies, and in particular, to a brightness mapping method and a backlight system based on MiniLED backlight. Background Art

[0002] In the field of display technologies, MiniLED backlights have gradually become a key technology for improving the quality of display images due to their excellent performance. However, in the existing regional dimming algorithms, problems such as loss of dark area details, overexposure in bright areas, and slow dimming response speed exist during the brightness mapping process, seriously affecting the visual effect of the display image and making it difficult to meet the user's demand for high-quality displays. Therefore, improvements to the existing technology are needed.

[0003] The above information is given as background information only to assist in understanding the present disclosure, and it is not determined or admitted whether any of the above content can be used as prior art relative to the present disclosure. Summary of the Invention

[0004] The present invention provides a brightness mapping method and a backlight system based on MiniLED backlight to solve the problems existing in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A brightness mapping method based on MiniLED backlight, comprising:

[0007] Analyze the input image to obtain image features;

[0008] According to the image features, adaptively divide the dimming regions;

[0009] For each dimming region, in combination with the human eye visual characteristics, implement the conversion from pixel brightness to MiniLED backlight brightness through non-linear mapping.

[0010] Optionally, the analyzing the input image to obtain image features includes:

[0011] Analyze the complexity of the image content of the input image; and analyze the brightness distribution characteristics of the input image;

[0012] The according to the image features, adaptively dividing the dimming regions includes:

[0013] When the dimming control requirement characterized by the image features is fine, divide small dimming regions;

[0014] When the dimming control requirement characterized by the image features is rough, divide large dimming regions;

[0015] Among them, the fine dimming control requirements include cases with high image content complexity and large brightness changes; the rough dimming control requirements include cases with low image content complexity and small brightness changes.

[0016] Optionally, analyzing the image content complexity of the input image includes:

[0017] Analyze the brightness distribution and contrast, and obtain the image content complexity by calculating one or more of the brightness histogram, brightness variance or standard deviation, and judging the dynamic range;

[0018] Perform spatial detail and edge detection, extract image edges using the Sobel algorithm or Canny algorithm, count the number of edges, or quantify the texture complexity using the gray-level co-occurrence matrix or Gabor filter;

[0019] Perform frequency domain analysis, and judge the complexity based on the proportion of high-frequency and low-frequency components in the Fourier transform result; for video scenes, calculate the change amplitude between adjacent frames by the optical flow method or frame difference method, and analyze the motion characteristics;

[0020] Calculate the brightness difference between adjacent dimming regions and analyze the regional correlation.

[0021] Optionally, the method further includes formulating the following grading strategy based on the image content complexity: low complexity scene, with large areas of single brightness, low dynamic range and / or sparse edge features;

[0022] Medium complexity scene, with medium dynamic range, local light and dark alternation and / or a small number of detail features;

[0023] High complexity scene, with high dynamic range, dense edges, dense texture and / or fast light and dark change features.

[0024] Optionally, the method further includes adopting the following dimming methods for different complexity scenes: for low complexity scenes, adopt large dimming regions and reduce the dimming frequency;

[0025] For medium complexity scenes, adopt medium dimming regions and dynamically adjust the dimming frequency;

[0026] For high complexity scenes, adopt small dimming regions and increase the dimming frequency.

[0027] Optionally, the method further includes determining the image content complexity according to the following process:

[0028] Perform preprocessing on the image for noise reduction and RGB to YUV color space conversion;

[0029] Extract brightness variance, edge density, and dynamic range features;

[0030] Generate a quantization score through a weighted formula;

[0031] Divide the complexity level according to a preset threshold;

[0032] Adjust the dimming area parameters in combination with the complexity level.

[0033] Optionally, in combination with the human eye visual characteristics, the conversion from pixel brightness to MiniLED backlight brightness is realized through non-linear mapping, including:

[0034] Statistically analyze the pixel brightness distribution in each dimming area, and in combination with the human eye visual characteristic curve, increase the MiniLED backlight brightness gain for dark area pixels and limit the MiniLED backlight brightness increase amplitude for bright area pixels.

[0035] Optionally, the method further includes:

[0036] Formulate a dimming strategy based on the inter-frame prediction technology;

[0037] Adjust the MiniLED backlight brightness in advance according to the brightness adjustment strategy;

[0038] Optimize the dimming time interval in combination with the human eye visual persistence characteristic.

[0039] Optionally, the inter-frame prediction technology includes:

[0040] Divide the current frame image into multiple blocks of the same size;

[0041] Search for a block matching the current block in the reference frame, evaluate the matching degree through the sum of absolute differences criterion, and determine the motion vector;

[0042] Obtain the matching block information from the reference frame according to the motion vector, and calculate the current block prediction value;

[0043] Combine the prediction values of each block to generate the current frame prediction frame, compare it with the actual current frame, calculate the error information of each dimming area, and in combination with the MiniLED backlight zoning situation, determine the brightness adjustment strategy for each dimming area.

[0044] The present invention also provides a MiniLED backlight system, including a processor and a memory, where the memory stores a computer program, and when the processor executes the program, it implements the brightness mapping method described in any one of the above.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] A brightness mapping method and a backlight system based on MiniLED backlight provided by the present invention analyze the characteristics of the input image, adaptively divide the dimming area, and perform non-linear mapping in combination with the human eye visual characteristics, effectively improving the contrast of the display screen, retaining image details to the greatest extent, significantly improving the picture quality problems caused by the loss of dark area details and overexposure in the bright area, and making the display effect more in line with the visual needs of the human eye.

[0047] The present invention has other characteristics and advantages, which will be obvious from the accompanying drawings incorporated herein and the subsequent detailed description, or will be described in detail in the accompanying drawings incorporated herein and the subsequent detailed description, and these accompanying drawings and detailed description are used together to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 is a flowchart of a brightness mapping method based on MiniLED backlight provided in Embodiment 1 of the present invention;

[0050] Figure 2 is a flowchart of a brightness mapping method based on MiniLED backlight provided in Embodiment 1 of the present invention;

[0051] Figure 3 is a flowchart of a brightness mapping method based on MiniLED backlight provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] To describe in detail the possible application scenarios, technical principles, specific implementable solutions, achievable purposes and effects, etc. of the present application, the following will be described in detail in combination with the specific embodiments listed and the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, so they are only examples and cannot be used to limit the protection scope of the present application.

[0053] Reference to "embodiment" in this text means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing at various positions in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or relevance to other embodiments. In principle, in this application, as long as there is no technical contradiction or conflict, the various technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0054] Unless otherwise defined, the meanings of the technical terms used in this text are the same as those commonly understood by those skilled in the technical field to which this application belongs; the use of the relevant terms in this text is only for describing specific embodiments and is not intended to limit this application.

[0055] In the description of this application, the phrase "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: there is A, there is B, and there is both A and B at the same time. In addition, the character " / " in this text generally represents an "or" logical relationship between the associated objects before and after.

[0056] In this application, terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantitative, primary or secondary, or sequential relationships between these entities or operations.

[0057] Without further limitation, in this application, the expressions such as "include", "comprise", "have" or other similar expressions used in a statement are intended to cover non-exclusive inclusion. These expressions do not exclude that there may be additional elements in the process, method or product including the said elements, so that the process, method or product including a series of elements may not only include those defined elements, but also include other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0058] The same as the understanding in the "Examination Guidelines", in this application, expressions such as "greater than", "less than", "exceeding" are understood not to include the number itself; expressions such as "above", "below", "within" are understood to include the number itself. In addition, in the description of the embodiments of this application, the meaning of "a plurality of" is two or more (including two), and similar expressions related to "many" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise specifically defined.

[0059] In the description of the embodiments of the present application, the spatially related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "perpendicular", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the specific embodiment or the drawings, and is only for the convenience of describing the specific embodiments of the present application or for the reader's understanding, rather than indicating or implying that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, it should not be construed as a limitation to the embodiments of the present application.

[0060] Unless otherwise clearly specified or limited, in the description of the embodiments of the present application, the terms such as "installed", "connected", "connected", "fixed", "set", etc. should be understood in a broad sense. For example, the "connection" may be a fixed connection, a detachable connection, or an integral setting; it may be a mechanical connection, an electrical connection, or a communication connection; it may be directly connected, or indirectly connected through an intermediate medium; it may be the communication inside two components or the interaction relationship between two components. For those skilled in the art to which the present application pertains, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific circumstances.

[0061] Please refer to Figure 1 , the embodiments of the present invention provide a brightness mapping method based on MiniLED backlight, including:

[0062] S0. Image input and preprocessing.

[0063] Input the image to be displayed into the system, perform preprocessing operations such as noise reduction and contrast enhancement on the image, and at the same time perform operations such as noise reduction and color space conversion (RGB→YUV to extract the luminance component) to improve the image quality and provide a basis for subsequent processing.

[0064] S1. Analyze the input image to obtain image features.

[0065] In this step, after the image is input, it is analyzed from two aspects. On the one hand, analyze the complexity of the image content of the input image, and on the other hand, analyze the luminance distribution characteristics of the input image.

[0066] S11. Feature extraction.

[0067] Specifically, the extracted features are used to calculate indicators such as luminance variance, edge density, and dynamic range, and use the histogram to statistically analyze the luminance variance / standard deviation and the dynamic range to analyze the luminance distribution and contrast.

[0068] Specifically, the extracted features include:

[0069] Edge density: extract image edge statistics density through edge detection algorithms such as Sobel and Canny, use gray-level co-occurrence matrix (GLCM) or Gabor filter to quantify texture analysis spatial details; perform Fourier transform to analyze frequency domain features;

[0070] Dynamic range: In video scenes, the change range of adjacent frames is calculated by the optical flow method or the frame difference method to analyze motion characteristics. For example, the motion vector is calculated by the optical flow method. If the difference between adjacent frames exceeds a threshold (such as the pixel change rate > 10%), it is determined to be a dynamic scene and the dimming frequency needs to be increased to avoid ghosting.

[0071] Brightness variance: Calculates the brightness difference between adjacent backlight partitions to analyze regional correlation.

[0072] Specifically, when calculating the brightness histogram of an image, the complexity is judged based on the degree of distribution concentration or dispersion. If the distribution is concentrated, such as a large area of ​​pure black or pure white, the complexity is low; if the distribution is dispersed, with light and dark interlaced, the complexity is high. At the same time, the brightness variance or standard deviation is calculated. The larger the variance, the more drastic the brightness change, and the higher the complexity. In addition, high dynamic range (HDR) images usually contain more light and dark details and are more complex.

[0073] Extract complexity through spatial details and edge detection. Use edge detection algorithms such as Sobel and Canny to extract image edges and count the number of edges per unit area. The more edges there are, the higher the complexity.

[0074] Texture complexity is quantified using gray-level co-occurrence matrices (GLCMs) or Gabor filters. High-frequency details, such as leaves and hair, can significantly increase the complexity.

[0075] When performing frequency domain analysis using Fourier transform, images with a high proportion of high-frequency components have rich details and higher complexity; images dominated by low-frequency components have more smooth areas and lower complexity.

[0076] Motion feature analysis, also known as dynamic content analysis, is used to calculate the change range of adjacent frames through the optical flow method or frame difference method in video scenes. Dynamic scenes, such as fast motion, require higher dimming frequencies and are more complex.

[0077] Regional correlation analysis calculates the brightness difference between adjacent dimming areas. The larger the difference, such as a starry sky map, the higher the dimming difficulty and complexity.

[0078] The extracted features are comprehensively scored using a weighted formula, and the image is ultimately divided into low, medium, and high complexity levels. The dimming strategy is then dynamically adjusted accordingly to achieve a balance between image quality and power consumption.

[0079] S12. Comprehensive score.

[0080] Specifically, a quantitative score is generated through a weighted formula.

[0081] For example, the weighting formula is as follows: complexity=0.5×luminance variance+0.3×edge density+0.2×dynamic range.

[0082] S13. Make hierarchical decisions.

[0083] In this step, the image is divided into low, medium and high complexity levels according to the comprehensive score divided by the preset threshold, and the dimming strategy is dynamically adjusted accordingly to achieve a balance between image quality and power consumption.

[0084] In one embodiment, the numerical range of complexity and the corresponding level are as follows:

[0085] 0-30 is low, 30-70 is medium, 70-100 is high;

[0086] The image content is divided into low complexity (Level 1), medium complexity (Level 2), and high complexity (Level 3).

[0087] S2. Adaptively divide the dimming areas according to the image features.

[0088] In this step, based on the analysis results of the image content complexity and brightness distribution characteristics, according to the adaptive grid partitioning algorithm, the dimming area size is dynamically adjusted according to the image complexity and brightness change degree, and the pre-processed image is partitioned. By calculating the image's gradient, entropy and other features, combined with the image content complexity classification results, the boundaries of each area are determined to achieve reasonable image division.

[0089] When the image content is complex and the brightness changes greatly, it is divided into smaller dimming areas to achieve fine dimming; when the dimming control requirements represented by the image features are extensive, that is, the image content is complex and the brightness changes are small, it is divided into larger dimming areas to reduce the computational complexity. Specifically, the boundaries of each area are determined by calculating the gradient, entropy and other features of the image.

[0090] Specifically, the scenarios corresponding to the three levels of complexity are as follows:

[0091] Low-complexity scenes: with large-area single brightness, low dynamic range and / or sparse edge features, in this case, use a larger dimming area, reduce the dimming frequency, and reduce backlight power consumption. Specifically, when the brightness range is ≤300 nits (such as night scenes or single background images), use a large dimming area (such as 16×16 partitions) and a dimming frequency of ≤60Hz.

[0092] Medium complexity scenario: It has features such as medium dynamic range, local light and dark alternation, and a small amount of details. It adopts medium-sized dimming areas, dynamically adjusts the frequency, and balances image quality and power consumption. Specifically, through edge density determination, if the number of edges < 100 / pixel² and the texture energy < 0.1 (GLCM calculated value), it is classified as medium complexity and dynamic dimming is adopted (such as 8×8 partitions, frequency 120Hz);

[0093] High complexity scenario: It has the characteristics of high dynamic range (HDR), dense edges, dense texture, and rapid light and dark changes. It adopts fine-grained dimming areas, high-frequency dimming, and combines a halo suppression algorithm to further improve the display effect. Specifically, when the edge density ≥ 200 / pixel² and the texture energy ≥ 0.3, it is determined as high complexity, and small dimming areas (such as 4×4 partitions) are adopted, and high-frequency dimming (≥ 240Hz) is enabled.

[0094] Taking the HDR movie scene as an example, its brightness variance is 2500 (σ≈50), the edge density is 180 / pixel², and the dynamic range is 1200 nits. It is determined as a high complexity scene according to the threshold. The system automatically divides 4×4 dimming areas, the dimming frequency is increased to 240Hz, and non-linear mapping is adopted (dark area gain 1.3, bright area gain 0.9). Finally, the signal-to-noise ratio of dark area details is increased by 45%, and overexposure in the bright area is reduced by 55%.

[0095] Furthermore, the method of this embodiment further includes:

[0096] S3. For each dimming area, in combination with the human eye visual characteristics, realize the conversion from pixel brightness to the brightness of the MiniLED backlight through non-linear mapping.

[0097] In this step, for each dimming area, the pixel brightness distribution within the area is statistically analyzed. Combining with the human eye visual characteristic curve, the target brightness value of the MiniLED backlight in this area is calculated. Using the non-linear mapping function, the image pixel brightness is mapped to the backlight brightness range. For dark area pixels, the backlight brightness gain is appropriately increased, and for bright area pixels, the increase amplitude of the backlight brightness is restricted, so as to achieve precise control of the brightness of different areas.

[0098] Exemplarily, for dark area pixels, the backlight brightness gain is appropriately increased, and the gain coefficient is set between 1.2 - 1.5 to show dark area details; for bright area pixels, the increase amplitude of the backlight brightness is restricted, and the gain coefficient is set between 0.8 - 1.0 to ensure the color saturation and details in the bright area.

[0099] Furthermore, the method further includes:

[0100] S41. Based on the inter-frame prediction technology, formulate a dimming strategy.

[0101] First, divide the current frame image into several blocks of the same size, such as blocks with sizes of 2X2, 4×4, 8×8, or 16×16 pixels, etc.

[0102] Then, search for the block in the reference frame that best matches the current block (the reference frame can be forward, backward, or bidirectional). Use common search algorithms such as full search, diamond search, and three-step search, and use criteria such as sum of absolute differences (SAD) to evaluate the matching degree between the current block and the reference block. Find the matching block with the smallest error, and use its relative position difference as the motion vector.

[0103] Next, according to the obtained motion vector, obtain the corresponding matching block information from the reference frame, and calculate the predicted value of the current block through a certain algorithm.

[0104] Finally, combine the predicted values of each block to form the predicted frame of the current frame. Compare and analyze the predicted frame with the actual current frame, and calculate the error information of each region, etc. According to this information, combined with the zoning situation of the MiniLED backlight, determine the brightness adjustment strategy for each zone.

[0105] It can be understood that if the difference between the predicted frame and the current frame in a certain region is small, it means that the content in this region does not change much, and the brightness of the MiniLED backlight in this region can be appropriately reduced; if the difference is large, it may be necessary to increase the brightness or maintain the original brightness to ensure the clear and accurate display of the image.

[0106] S42. Adjust the brightness of the MiniLED backlight in advance according to the brightness adjustment strategy.

[0107] Monitor the change of the image in real time, use the inter-frame prediction technology to analyze the difference between adjacent frame images, and predict the brightness distribution of the next frame image. According to the prediction result, adjust the brightness of the MiniLED backlight in advance, combine the characteristics of human visual persistence, reasonably set the dimming time interval, achieve a fast response of dynamic dimming, and ensure the stability and smoothness of the display screen. At the same time, adjust the number of backlight zones, brightness step, and refresh rate in combination with the grading result of the image content complexity.

[0108] S43. Optimize the dimming time interval in combination with the characteristics of human visual persistence.

[0109] By optimizing the dimming time interval, make the brightness change smoother, avoid flicker phenomena, achieve a significant improvement in the dimming response speed, the response time can reach <5ms, and the algorithm delay is reduced to within 1 frame.

[0110] Taking the video scene as an example, in the video scene, the dynamic dimming mechanism quickly adjusts the brightness of the MiniLED backlight by means of inter-frame prediction technology:

[0111] Inter-frame prediction: The current frame image is divided into blocks of sizes such as 2X2 and 4×4. In the reference frame (forward, backward, or bidirectional), algorithms such as full search are used to find the matching block, and the motion vector is determined based on the sum of absolute differences criterion. Information is obtained from the reference frame according to the motion vector to calculate the predicted value of the current block, and the predicted frame is combined. By comparing the predicted frame with the current frame, the brightness adjustment strategy for each partition is determined. If the difference is small, the brightness is reduced; if the difference is large, the brightness is maintained or increased.

[0112] Dimming interval optimization: Combining the characteristics of the human eye's persistence of vision, through experiments and data analysis, the dimming time interval is reasonably set to avoid flicker, making the dimming response time < 5ms and the algorithm delay reduced to within 1 frame, improving the display quality of dynamic images.

[0113] Image complexity analysis: When extracting features, in addition to calculating the brightness difference between adjacent dimming regions, the impact on the dimming difficulty and complexity is also analyzed. If the brightness difference is large, the image content complexity is determined to be high, providing a basis for dimming region division and halo suppression.

[0114] Halo suppression: After determining the halo risk area, the brightness values are sampled at 1-pixel intervals along the normal direction of the edge pixel boundary, and a brightness gradient distribution map is generated through differential operation. The area where the brightness gradient change rate is greater than 0.5 is the compensation area, and the compensation amount is allocated according to the proportion of the gradient value to achieve a natural transition at the edge and suppress the halo.

[0115] Based on the foregoing embodiments, an embodiment of the present invention further provides a MiniLED backlight system, including a processor and a memory. The memory stores a computer program, and when the processor executes the program, the above-mentioned brightness mapping method is implemented.

[0116] In the present invention, through precise regional dimming control and non-linear brightness mapping, the contrast between the dark area and the bright area is effectively enhanced, making the display image more layered, the colors more vivid, and the signal-to-noise ratio of the details in the dark area increased by 40%. In addition, the non-linear brightness mapping and the adaptive zoning strategy fully consider the image details and the human eye's visual characteristics. While improving the contrast, the image detail information is maximally retained, the overexposure phenomenon in the bright area is reduced by 60%, and the color accuracy is improved. Finally, the dynamic dimming mechanism combined with the inter-frame prediction technology realizes a fast response to dynamic images, effectively reducing the image smear and blur phenomena, improving the display quality of dynamic images, greatly enhancing the dimming response speed, with the response time < 5ms and the algorithm delay reduced to within 1 frame.

[0117] Finally, it should be noted that although the above embodiments have been described in the text and drawings of the specification of this application, the scope of the invention protection of this application cannot be limited thereby. Any technical solutions obtained by equivalent structure or equivalent process substitution or modification based on the substantial concept of this application and using the content recorded in the text and drawings of the specification of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, etc., are all included in the scope of the invention protection of this application.

Claims

1. A brightness mapping method based on MiniLED backlight, characterized in that: include: Analyze the input image and obtain image features; Adaptively dividing the dimming area according to the image features; For each dimming area, combined with the visual characteristics of the human eye, the conversion of pixel brightness to MiniLED backlight brightness is achieved through nonlinear mapping.

2. The brightness mapping method based on MiniLED backlight according to claim 1, characterized in that: The step of analyzing the input image to obtain image features includes: Analyzing the image content complexity of the input image; and, analyzing the brightness distribution characteristics of the input image; The step of adaptively dividing the dimming areas according to the image features includes: When the dimming control requirements represented by image features are fine, small dimming areas are divided; When the dimming control requirements represented by image features are extensive, large dimming areas are divided; The dimming control requirement is fine, including the case where the image content is highly complex and the brightness changes greatly; the dimming control requirement is rough, including the case where the image content is less complex and the brightness changes little.

3. The brightness mapping method based on MiniLED backlight according to claim 2, characterized in that: The step of analyzing the input image to obtain image features includes: Analyze brightness distribution and contrast, and obtain image content complexity by calculating brightness histogram, brightness variance or standard deviation, and judging dynamic range; Perform spatial detail and edge detection, use the Sobel algorithm and Canny algorithm to extract image edges, count the number of edges, or use the gray-level co-occurrence matrix and Gabor filter to quantify texture complexity; Conduct frequency domain analysis and judge the complexity based on the proportion of high-frequency and low-frequency components in the Fourier transform results; For video scenes, the change amplitude of adjacent frames is calculated by optical flow method or frame difference method to analyze motion characteristics; Calculate the brightness difference between adjacent dimming areas and analyze the regional correlation.

4. The brightness mapping method based on MiniLED backlight according to claim 1, characterized in that: Also includes: Based on the complexity of image content, the following classification strategy is formulated: Low-complexity scenes with large areas of single brightness, low dynamic range, and / or sparse edge features; Moderately complex scenes with moderate dynamic range, localized light and dark alternations, and / or low levels of detail; High-complexity scenes with high dynamic range, dense edges, dense textures, and / or fast changes in brightness and darkness.

5. The brightness mapping method based on MiniLED backlight according to claim 4, characterized in that: It also includes the following dimming methods for scenes of different complexity: For low-complexity scenes, a large dimming area is used to reduce the dimming frequency; For scenes with medium complexity, a medium dimming area is used to dynamically adjust the dimming frequency; For highly complex scenes, a small dimming area is used to increase the dimming frequency.

6. The brightness mapping method based on MiniLED backlight according to claim 5, characterized in that: It also includes determining the complexity of image content according to the following process: Perform image noise reduction and RGB to YUV color space conversion preprocessing; Extract brightness variance, edge density, and dynamic range features; Generate a quantitative score through weighted formula calculation; Divide the complexity level according to the preset threshold; Adjust the dimming zone parameters based on the complexity level.

7. The brightness mapping method based on MiniLED backlight according to claim 1, characterized in that: The method combines the visual characteristics of the human eye and realizes the conversion of pixel brightness to MiniLED backlight brightness through nonlinear mapping, including: Statistics are collected on the pixel brightness distribution in each dimming area, and combined with the visual characteristic curve of the human eye, the brightness gain of the MiniLED backlight source is increased for pixels in the dark area, and the brightness increase of the MiniLED backlight source is limited for pixels in the bright area.

8. The brightness mapping method based on MiniLED backlight according to claim 7, characterized in that: Also includes: Develop dimming strategies based on inter-frame prediction technology; Adjust the brightness of the MiniLED backlight source in advance according to the brightness adjustment strategy; Combined with the visual persistence characteristics of the human eye, the dimming time interval is optimized.

9. The brightness mapping method based on MiniLED backlight according to claim 8, characterized in that: The inter-frame prediction technology includes: Divide the current frame image into multiple blocks of the same size; Search the reference frame for a block that matches the current block, evaluate the degree of match by the absolute error and criterion, and determine the motion vector; Obtain matching block information from the reference frame according to the motion vector and calculate the current block prediction value; The prediction values ​​of each block are combined to generate the current frame prediction frame, which is compared with the actual current frame to calculate the error information of each dimming area. Combined with the MiniLED backlight partitioning situation, the brightness adjustment strategy of each dimming area is determined.

10. A MiniLED backlight system, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the processor executes the program, the brightness mapping method according to any one of claims 1 to 9 is implemented.

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