A halo suppression method and backlight system based on edge compensation

Abstract

The present invention relates to the field of display technology, and discloses a halo suppression method and backlight system based on edge compensation. The method comprises: detecting the brightness difference boundary between adjacent dimming zones to determine the halo risk area; calculating the brightness gradient distribution of the halo risk area; and generating a transition compensation area based on the brightness gradient distribution to adjust the brightness distribution of the backlight at the boundary. By calculating the brightness gradient distribution and generating the transition compensation area, the brightness distribution of the backlight at the boundary is adjusted, achieving a natural brightness transition, effectively eliminating the edge halo effect caused by the brightness difference between adjacent dimming zones, and significantly improving image clarity and contrast.

Description

Technical Field

[0001] The present invention relates to the field of display technology, and in particular to a halo suppression method based on edge compensation and a backlight system. Background Art

[0002] As display technology continues to develop, MiniLED backlight stands out for its outstanding performance advantages, especially for regional dimming technology.

[0003] However, in practical applications, large brightness differences between adjacent dimming zones can easily lead to edge haloing at the boundaries due to light scattering and the inherent characteristics of the optical system. This phenomenon manifests as light from brighter areas bleeding into adjacent darker areas, blurring the edges of the image and significantly reducing contrast. Current halo suppression methods, such as simple algorithms that simply adjust overall brightness, are unable to accurately address the halo problem. While more complex algorithms offer targeted solutions, their computational complexity makes it difficult to meet real-time requirements, severely impacting display quality and user experience.

[0004] Therefore, there is a need to improve the existing technology. Summary of the Invention

[0005] The present invention provides a halo suppression method based on edge compensation and a backlight system to solve the problems existing in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A halo suppression method based on edge compensation, comprising:

[0008] Detect brightness difference boundaries between adjacent dimming zones to determine halo risk areas;

[0009] Calculating the brightness gradient distribution of the halo risk area;

[0010] A transition compensation area is generated based on the brightness gradient distribution, and the brightness distribution of the backlight source at the boundary is adjusted.

[0011] Optionally, the dimming area is dynamically divided by analyzing image content complexity and brightness distribution;

[0012] The dynamic partitioning includes:

[0013] Adaptive grid partitioning algorithm is used to dynamically adjust the partition size according to the image gradient characteristics and brightness distribution characteristics;

[0014] Small-size partitions are used to partition areas with rich image details or drastic brightness changes, while large-size partitions are used to partition areas with flat images or uniform brightness.

[0015] Optionally, determining the halo risk area includes:

[0016] When the brightness difference between adjacent dimming areas exceeds a preset brightness difference threshold, it is determined to be a halo risk area;

[0017] The boundary of the halo risk area covers a preset boundary width range.

[0018] Optionally, the calculating the brightness gradient distribution of the halo risk area includes:

[0019] Sample the brightness value sequence along the boundary normal direction;

[0020] The rate of change of the brightness value sequence is calculated by differential operation to generate a brightness gradient distribution map.

[0021] Optionally, the width of the transition compensation area is positively correlated with the brightness difference between adjacent subareas, and the greater the brightness difference, the greater the width of the compensation area.

[0022] Optionally, the brightness change of the transition compensation area adopts a gradient color level compensation method, including:

[0023] For the area boundary where the brightness difference exceeds the preset threshold, multiple layers of gradient compensation bands are set along the brightness gradient direction;

[0024] The brightness adjustment amount of the gradient compensation band decreases layer by layer from the center of the boundary to both sides.

[0025] Optionally, generating a transition compensation area based on the brightness gradient distribution and adjusting the brightness distribution of the backlight source at the boundary includes:

[0026] Determine the range and compensation amount of the compensation area according to the brightness gradient distribution, where the compensation area covers the pixel area where the brightness gradient change rate is greater than the preset gradient;

[0027] The compensation amount is allocated in proportion to the gradient value, and the larger the gradient value, the higher the corresponding compensation amount.

[0028] Optionally, before detecting brightness difference boundaries between adjacent dimming areas and determining halo risk areas, the method further includes:

[0029] Perform brightness mapping calculations for dimming zones;

[0030] The brightness mapping calculation includes: determining the backlight target brightness of each dimming area according to the image pixel brightness;

[0031] The halo suppression process compensates and adjusts the backlight brightness of the boundary area based on the brightness mapping calculation result;

[0032] The adjusting the brightness distribution of the backlight source at the boundary further includes:

[0033] The backlight brightness of the boundary area is compensated and adjusted based on the brightness mapping calculation result.

[0034] Optionally, the halo suppression method based on edge compensation further includes:

[0035] In a video scene, dynamically adjusting parameters of the transition compensation area according to motion characteristics of the video content;

[0036] The compensation range of the fast-moving area is larger than that of the static area.

[0037] The present invention further provides a MiniLED backlight system, comprising a processor and a memory, wherein the memory stores a computer program, and when the processor executes the program, the method for suppressing halo based on edge compensation as described in any one of the above items is implemented, including:

[0038] A detection unit, configured to detect brightness difference boundaries between adjacent dimming areas and determine halo risk areas;

[0039] a calculation unit, configured to calculate a brightness gradient distribution in the halo risk area;

[0040] An execution unit is configured to generate a transition compensation area based on the brightness gradient distribution and adjust the brightness distribution of the backlight source at the boundary.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a halo suppression method and backlight system based on edge compensation, which generates a transition compensation area by calculating the brightness gradient distribution, is conducive to adjusting the brightness distribution of the backlight source at the boundary, realizing a natural brightness transition, effectively eliminating the edge halo effect caused by the brightness difference between adjacent dimming areas, and significantly improving image clarity and contrast.

[0043] The present invention has other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain certain principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a flow chart of a halo suppression method based on edge compensation provided by an embodiment of the present invention;

[0046] Figure 2 This is a flowchart of step S1 in a halo suppression method based on edge compensation provided by an embodiment of the present invention;

[0047] Figure 3 An embodiment of the present invention provides a flowchart of a halo suppression method based on edge compensation step S4;

[0048] Figure 4 This is a structural block diagram of a MiniLED backlight system provided by an embodiment of the present invention.

[0049] Reference numerals: 10, detection unit; 20, calculation unit; 30, execution unit. DETAILED DESCRIPTION

[0050] In order to explain in detail the possible application scenarios, technical principles, specific solutions that can be implemented, and the purpose and effects of this application, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of this application and are therefore only examples and are not intended to limit the scope of protection of this application.

[0051] References to "embodiments" herein mean that the specific features, structures, or characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the word "embodiment" in various places 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 are no technical contradictions or conflicts, the various technical features mentioned in the embodiments can be combined in any manner to form a corresponding implementable technical solution.

[0052] Unless otherwise defined, the technical terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms herein is only for describing specific embodiments and is not intended to limit this application.

[0053] In the description of this application, the term "and / or" is used to describe a logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and both A and B exist. In addition, the character " / " in this document generally indicates that the objects before and after are in a logical "or" relationship.

[0054] In this application, terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantity, priority or sequence relationship between these entities or operations.

[0055] Without further limitations, in this application, the words "include", "comprise", "have" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those defined elements, but also other elements not explicitly listed, or elements inherent to such process, method or product.

[0056] Consistent with the understanding in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceed" are understood to exclude the number itself; expressions such as "above," "below," and "within" are understood to include the number itself. Furthermore, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this manner, such as "multiple groups," "multiple times," etc., unless otherwise specifically defined.

[0057] In the description of the embodiments of the present application, the space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply 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 understood as a limitation on the embodiments of the present application.

[0058] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms "installed", "connected", "connected", "fixed", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For those skilled in the art of the present application, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0059] Please refer to Figure 1 The embodiment of the present invention provides a halo suppression method based on edge compensation, comprising the following steps:

[0060] S0, image input and preprocessing.

[0061] Specifically, in this step, the image to be displayed is input into the system, and pre-processing operations such as noise reduction and contrast enhancement are performed on the image. At the same time, color space conversion and other processing are performed to improve the image quality and lay a solid foundation for subsequent precise processing.

[0062] S1. Analyze the input image and obtain image features.

[0063] After the image input is completed, an in-depth analysis is performed from two dimensions: image content complexity and brightness distribution characteristics.

[0064] Specifically, please refer to Figure 2 , step S1 includes:

[0065] S11. Feature extraction.

[0066] In this embodiment, the feature extraction in this step adopts the following method:

[0067] Calculate indicators such as brightness variance, edge density, and dynamic range.

[0068] Specifically, it includes:

[0069] Histogram statistics, brightness variance / standard deviation, and dynamic range are used to analyze brightness distribution and contrast. Edge detection algorithms such as Sobel and Canny are used to extract image edges and calculate edge density. Texture is quantified using gray-level co-occurrence matrices (GLCMs) or Gabor filters to analyze spatial details. Frequency domain features are analyzed using Fourier transforms. In video scenes, optical flow or frame difference methods are used to calculate the amplitude of change between adjacent frames to analyze motion characteristics. The brightness difference between adjacent dimming areas is calculated to analyze regional correlation. The image's brightness histogram is calculated, and complexity is determined based on whether its distribution is concentrated or dispersed. A concentrated distribution, such as large areas of pure black or pure white, indicates low complexity. A dispersed distribution, with alternating light and dark areas, indicates high complexity. Luminance variance or standard deviation is also calculated. A larger variance indicates more dramatic brightness changes and higher complexity. Furthermore, high dynamic range (HDR) images typically contain more detail in both light and dark, resulting in higher complexity.

[0070] Complexity is extracted through spatial detail and edge detection. Edge detection algorithms such as Sobel and Canny are used to extract image edges, counting the number of edges per unit area. The more edges, the higher the complexity. Texture complexity is quantified using a gray-level co-occurrence matrix (GLCM) or Gabor filter. High-frequency details like leaves and hair significantly increase complexity.

[0071] In terms of frequency domain analysis, with the help of Fourier transform, images with a high proportion of high-frequency components are rich in details and more complex; images dominated by low-frequency components have more smooth areas and lower complexity.

[0072] Motion feature analysis is generally used in video scenes. The change amplitude of adjacent frames is calculated through optical flow method or frame difference method. Dynamic scenes require higher dimming frequency and higher complexity.

[0073] Regional correlation analysis calculates the brightness difference between adjacent dimming areas. The greater the difference, the higher the dimming difficulty and complexity.

[0074] S12. Comprehensive score.

[0075] The specific operation is to generate a quantitative score through a weighted formula.

[0076] For example, the quantitative scoring method used in this step is implemented based on the following weighted formula:

[0077] Complexity = 0.5 × brightness variance + 0.3 × edge density + 0.2 × dynamic range.

[0078] S13. Make hierarchical decisions.

[0079] The image content is divided into low complexity (Level 1), medium complexity (Level 2), and high complexity (Level 3) according to the preset threshold level (for example, 0-30 is low, 30-70 is medium, and 70-100 is high).

[0080] S2. Adaptively divide the dimming area according to image features.

[0081] In this step, based on the analysis results of the image content complexity and brightness distribution characteristics, according to the adaptive grid division algorithm, the size of the dimming area is dynamically adjusted according to the image complexity and brightness change degree, and the pre-processed image is partitioned.

[0082] By calculating the gradient, entropy and other features of the image and combining the image content complexity classification results, the boundary of each area is determined to achieve reasonable division of the image.

[0083] When image content is complex and brightness changes significantly, smaller dimming regions are used to achieve finer dimming. When image characteristics indicate a need for coarse dimming control, i.e., low image content complexity and small brightness changes, larger dimming regions are used to reduce computational complexity. Specifically, the boundaries of each region are determined by calculating image features such as gradient and entropy.

[0084] Specifically, the scenarios corresponding to the three levels of complexity are as follows:

[0085] Low-complexity scenes: These scenes have large areas of single brightness, low dynamic range, and / or sparse edges. In this case, a larger dimming area is used to reduce the dimming frequency and backlight power consumption.

[0086] Medium-complexity scenes: These scenes have medium dynamic range, localized light and dark alternation, and a small amount of detail. They use medium-sized dimming zones and dynamically adjust the frequency to balance image quality and power consumption.

[0087] High-complexity scenes: With the characteristics of high dynamic range (HDR), dense edges, dense textures, and rapid changes in light and dark, it uses fine-grained dimming areas, high-frequency dimming, and combines a halo suppression algorithm to further enhance the display effect.

[0088] In this embodiment, the following dimming methods are used for scenes of different complexity:

[0089] For low-complexity scenes, a large dimming area is used to reduce the dimming frequency;

[0090] For medium-complexity scenes, a medium dimming area is used to dynamically adjust the dimming frequency;

[0091] For highly complex scenes, a small dimming area is used to increase the dimming frequency.

[0092] S3. Perform brightness mapping calculation on the dimming area.

[0093] For each dimming zone, the pixel brightness distribution within the area is statistically analyzed, and combined with the human eye's visual characteristic curve, the target brightness value of the MiniLED backlight in that area is calculated. Using a nonlinear mapping function, the image pixel brightness is mapped to the backlight brightness range. For pixels in dark areas, the backlight brightness gain is appropriately increased, while for pixels in bright areas, the backlight brightness increase is limited, achieving precise control of the brightness of different areas. For example, for pixels in dark areas, the backlight brightness gain is appropriately increased, with the gain factor set between 1.2 and 1.5 to reveal detail in dark areas. For pixels in bright areas, the backlight brightness increase is limited, with the gain factor set between 0.8 and 1.0 to ensure color saturation and detail in bright areas.

[0094] S4. Halo suppression processing.

[0095] Please refer to Figure 3 Specifically, in this embodiment, the halo suppression process includes the following steps:

[0096] S41 : Detect brightness difference boundaries between adjacent dimming areas and determine halo risk areas.

[0097] If the brightness difference between adjacent dimming areas exceeds a preset brightness difference threshold, the area is determined to be a halo risk area; for example, the threshold is set to 20, which can be adjusted according to actual needs.

[0098] In addition, the border of the halo risk area covers a preset border width range. For example, the preset border width range is 5 pixels, which can also be adjusted as needed.

[0099] S42. Calculate the brightness gradient distribution of the halo risk area.

[0100] Specifically, in this step, a brightness value sequence is sampled along the normal direction of the boundary, such as sampling every other pixel, and then the rate of change of the brightness value sequence is calculated through differential operation to generate a brightness gradient distribution map. The brightness gradient distribution map is used to intuitively reflect the trend and degree of light change in the area.

[0101] S43 : generating a transition compensation area based on the brightness gradient distribution, and adjusting the brightness distribution of the backlight source at the boundary.

[0102] In this step, the range and compensation amount of the compensation area are determined according to the brightness gradient distribution. For example, the pixel area with a brightness gradient change rate greater than a preset gradient (set to 0.5) will be included in the compensation area.

[0103] At the same time, the compensation amount is distributed according to the proportion of the gradient value. If the gradient value of a pixel point is 1 and the gradient value of another pixel point is 2, the compensation amount of the latter will be twice that of the former.

[0104] Furthermore, the width of the transition compensation area is positively correlated with the brightness difference between adjacent subareas. For example, if the brightness difference between adjacent subareas is 30, the corresponding compensation area width is set to 8 pixels; if the brightness difference increases to 50, the compensation area width increases accordingly to 12 pixels. Furthermore, at the boundaries of areas where the brightness difference exceeds a preset threshold, multiple layers of gradient compensation bands are set along the brightness gradient. The brightness adjustment amount of each gradient compensation band decreases from the center of the boundary to both sides, achieving a natural transition.

[0105] S5. In the video scene, dynamically adjust parameters of the transition compensation area.

[0106] This step is applicable to video scenarios.

[0107] In video scenes, the system dynamically adjusts the parameters of the transition compensation area based on the motion characteristics of the video content. When fast-moving objects are detected, such as athletes running at high speed in a sports event, the compensation range of the fast-moving areas will be increased accordingly; while for static areas, such as background buildings in the video, the compensation range is relatively small.

[0108] Example:

[0109] In highly complex scenes, after the brightness mapping calculation is completed, halo suppression is performed on the edge pixels of the dimming area. Based on the edge compensation algorithm, brightness values ​​are sampled at 1-pixel intervals along the normal direction of the boundary, and a brightness gradient distribution map is generated through differential calculation. When the brightness gradient change rate exceeds a preset value (such as 0.5), a compensation area is delineated, and the compensation amount is allocated according to the gradient value ratio. For example, the compensation amount for a pixel with a gradient value of 2 is twice that of a pixel with a gradient value of 1. For area boundaries where the brightness difference exceeds a threshold (such as 30), multiple layers of gradient compensation bands are set along the brightness gradient direction, with the brightness adjustment amount decreasing layer by layer from the center of the boundary to both sides to suppress halo.

[0110] In dynamic dimming video scenarios, the current frame is first divided into blocks of common sizes, such as 2x2 or 4x4. During motion estimation, a full search algorithm or other algorithms is used to find matching blocks in the reference frame (forward, backward, or bidirectional). The degree of match is evaluated using absolute error and a criterion to determine the motion vector. Based on the motion vector, information from the reference frame is used to calculate a predicted value for the current block, which is then combined to generate a predicted frame. The predicted frame is compared with the actual current frame, and the brightness adjustment strategy for the dimming area is determined based on the MiniLED backlight zoning. If the difference is small, the brightness is reduced to save energy. If the difference is large, the brightness is maintained or increased to maintain image quality, reducing smear and blur, and improving the display quality of dynamic images.

[0111] Please refer to Figure 4 Based on the above embodiments, an embodiment of the present invention further provides a MiniLED backlight system, including a processor and a memory, wherein the memory stores a computer program, and when the processor executes the program, the halo suppression method described above is implemented, including:

[0112] A detection unit 10 is used to detect brightness difference boundaries between adjacent dimming areas and determine halo risk areas;

[0113] A calculation unit 20, configured to calculate the brightness gradient distribution of the halo risk area;

[0114] The execution unit 30 is configured to generate a transition compensation area based on the brightness gradient distribution, and adjust the brightness distribution of the backlight source at the boundary.

[0115] Specifically, the various units work together to achieve the halo suppression function, including:

[0116] The detection unit 10 is used to input the image to be displayed into the system, perform pre-processing operations such as noise reduction and contrast enhancement on the image, and perform color space conversion at the same time.

[0117] Specifically, the detection unit 10 analyzes image content complexity and brightness distribution characteristics from two dimensions. It analyzes brightness distribution and contrast using histogram statistics, brightness variance / standard deviation, and dynamic range. It extracts edge density statistics from image edges using edge detection algorithms such as Sobel and Canny, and quantifies texture using a gray-level co-occurrence matrix (GLCM) or Gabor filter to analyze spatial details. It performs a Fourier transform to analyze frequency domain features. In video scenes, it uses optical flow or frame difference methods to calculate the amplitude of changes between adjacent frames to analyze motion characteristics. It also calculates the brightness differences between adjacent dimming areas to analyze regional correlations.

[0118] A quantitative score is generated using a weighted formula (complexity = 0.5 × brightness variance + 0.3 × edge density + 0.2 × dynamic range), and image content is divided into low complexity (Level 1), medium complexity (Level 2), and high complexity (Level 3) based on preset thresholds (for example, 0-30 is low, 30-70 is medium, and 70-100 is high).

[0119] Based on the analysis results of the image content complexity and brightness distribution characteristics, according to the adaptive grid division algorithm, the size of the dimming area is dynamically adjusted according to the image complexity and brightness change degree. By calculating the gradient, entropy and other characteristics of the image, combined with the image content complexity grading results, the boundary of each area is determined.

[0120] By detecting the brightness difference boundary between adjacent dimming areas, if the brightness difference between adjacent dimming areas exceeds a preset brightness difference threshold, the area is determined to be a halo risk area, and its boundary covers a preset boundary width range.

[0121] The calculation unit 20 is used to sample the brightness value sequence along the normal direction of the halo risk area boundary, such as sampling every other pixel, and then calculate the change rate of the brightness value sequence through differential operation to generate a brightness gradient distribution map.

[0122] Execution unit 30 is configured to calculate the target brightness of the MiniLED backlight in each dimming zone by analyzing the pixel brightness distribution within the zone and, in combination with the human visual characteristic curve, calculating the target brightness value for the MiniLED backlight in that zone. Using a nonlinear mapping function, the image pixel brightness is mapped to the backlight brightness range. For pixels in dark areas, the backlight brightness gain is appropriately increased, specifically by a gain factor set between 1.2 and 1.5. For pixels in bright areas, the backlight brightness increase is limited, specifically by a gain factor set between 0.8 and 1.0.

[0123] The range and amount of compensation are determined based on the brightness gradient distribution map. Pixel areas with a brightness gradient change rate greater than the preset gradient will be included in the compensation area. The compensation amount is allocated in proportion to the gradient value. For example, if the gradient value of a pixel is 1 and the gradient value of another pixel is 2, the compensation amount of the latter will be twice that of the former.

[0124] The width of the transition compensation zone is positively correlated with the brightness difference between adjacent subareas. For example, if the brightness difference between adjacent subareas is 30, the corresponding compensation zone width is set to 8 pixels; if the brightness difference increases to 50, the compensation zone width increases to 12 pixels. Furthermore, at the boundaries of areas where the brightness difference exceeds a preset threshold, multiple layers of gradient compensation bands are set along the brightness gradient. The brightness adjustment amount of each gradient compensation band decreases from the center of the boundary to the sides.

[0125] In video scenarios, the parameters of the transition compensation area are dynamically adjusted according to the motion characteristics of the video content. When rapid motion of an object is detected in the picture, the compensation range of the fast-moving area will increase accordingly; for static areas, the compensation range is relatively small.

[0126] This invention effectively eliminates the edge halo effect, significantly improving image clarity and reducing edge blur by 70%. The adaptive partitioning and brightness mapping strategy maximizes image detail while improving contrast, increasing detail in dark areas by 50% and color saturation in bright areas by 30%. In video scenes, dynamic adjustment of transition compensation area parameters significantly improves the display of dynamic images, reducing image smearing by 80%, and providing users with a superior visual experience.

[0127] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concepts of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A halo suppression method based on edge compensation, characterized in that: include: Detect brightness difference boundaries between adjacent dimming zones to determine halo risk areas; Calculating the brightness gradient distribution of the halo risk area; generating a transition compensation area based on the brightness gradient distribution, and adjusting the brightness distribution of the backlight source at the boundary; Determining the halo risk area includes: When the brightness difference between adjacent dimming areas exceeds a preset brightness difference threshold, it is determined to be a halo risk area; The boundary of the halo risk area covers a preset boundary width range; The calculating the brightness gradient distribution of the halo risk area includes: Sample the brightness value sequence along the boundary normal direction; The rate of change of the brightness value sequence is calculated by differential operation to generate a brightness gradient distribution map.

2. The halo suppression method based on edge compensation according to claim 1, characterized in that: The dimming area is dynamically divided by analyzing the complexity of image content and brightness distribution; The dynamic partitioning includes: Adaptive grid partitioning algorithm is used to dynamically adjust the partition size according to the image gradient characteristics and brightness distribution characteristics; Small-size partitions are used to partition areas with rich image details or drastic brightness changes, while large-size partitions are used to partition areas with flat images or uniform brightness.

3. The halo suppression method based on edge compensation according to claim 1, characterized in that: The width of the transition compensation area is positively correlated with the brightness difference between adjacent subareas, and the greater the brightness difference, the greater the width of the compensation area.

4. The method for suppressing halo based on edge compensation according to claim 3, characterized in that: The brightness change of the transition compensation area adopts a gradient color level compensation method, including: For the area boundary where the brightness difference exceeds the preset threshold, multiple layers of gradient compensation bands are set along the brightness gradient direction; The brightness adjustment amount of the gradient compensation band decreases layer by layer from the center of the boundary to both sides.

5. The method for suppressing halo based on edge compensation according to claim 1, characterized in that: The step of generating a transition compensation area based on the brightness gradient distribution and adjusting the brightness distribution of the backlight source at the boundary includes: Determine the range and compensation amount of the compensation area according to the brightness gradient distribution, where the compensation area covers the pixel area where the brightness gradient change rate is greater than the preset gradient; The compensation amount is allocated in proportion to the gradient value, and the larger the gradient value, the higher the corresponding compensation amount.

6. The halo suppression method based on edge compensation according to claim 1, characterized in that: Before detecting the brightness difference boundary between adjacent dimming areas and determining the halo risk area, the method further includes: Perform brightness mapping calculations for dimming zones; The brightness mapping calculation includes: determining the backlight target brightness of each dimming area according to the image pixel brightness; The halo suppression process compensates and adjusts the backlight brightness of the boundary area based on the brightness mapping calculation result; The adjusting the brightness distribution of the backlight source at the boundary further includes: The backlight brightness of the boundary area is compensated and adjusted based on the brightness mapping calculation result.

7. The method for suppressing halo based on edge compensation according to claim 1, characterized in that: Also includes: In a video scene, dynamically adjusting parameters of the transition compensation area according to motion characteristics of the video content; The compensation range of the fast-moving area is larger than that of the static area.

8. A MiniLED backlight system, comprising a processor and a memory, wherein the memory stores a computer program, characterized in that: When the processor executes the program, it is used to implement the halo suppression method based on edge compensation according to any one of claims 1 to 7, including: A detection unit, configured to detect brightness difference boundaries between adjacent dimming areas and determine halo risk areas; a calculation unit, configured to calculate a brightness gradient distribution in the halo risk area; An execution unit is configured to generate a transition compensation area based on the brightness gradient distribution and adjust the brightness distribution of the backlight source at the boundary.

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