Hhalo suppression method based on edge compensation and backlight system

Abstract

The invention relates to the technical field of display, and discloses a halo suppression method based on edge compensation and a backlight system.The method comprises the steps that the brightness difference boundary between adjacent dimming areas is detected, and a halo risk area is determined; calculating brightness gradient distribution of the halo risk area; and generating a transition compensation area based on the brightness gradient distribution, and adjusting the brightness distribution of the backlight source at the boundary. A transition compensation area is generated by calculating brightness gradient distribution, backlight brightness distribution at the boundary can be adjusted, natural brightness transition is achieved, the edge halo effect caused by brightness difference of adjacent dimming areas is effectively eliminated, and image definition and contrast are remarkably improved.

Description

Technical Field

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

[0002] In the current continuous development of display technologies, MiniLED backlights stand out with their excellent performance advantages and are particularly suitable for local dimming technologies.

[0003] However, in practical applications, once there is a large brightness difference between adjacent dimming regions, affected by light scattering and the inherent characteristics of the optical system, an edge halo effect is extremely likely to occur at the region boundaries. This phenomenon is manifested as the light in the brighter region penetrating into the adjacent darker region, resulting in blurred edges of the picture and a significant decrease in contrast. In current halo suppression methods, in simple algorithms, only the overall brightness is simply adjusted, and the halo problem cannot be accurately addressed; in other complex algorithms, although they are targeted, the calculation process is complex and it is difficult to meet the real-time requirements, seriously affecting the display effect 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] Detecting the brightness difference boundary between adjacent dimming regions to determine the halo risk region;

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

[0010] Generating a transition compensation region based on the brightness gradient distribution to adjust the brightness distribution of the backlight source at the boundary.

[0011] Optionally, the dimming regions are dynamically divided by analyzing the complexity of the image content and the brightness distribution;

[0012] The dynamic division includes:

[0013] According to the image gradient feature and the brightness distribution feature, using an adaptive grid division algorithm to dynamically adjust the partition size;

[0014] Dividing small-size partitions for regions with rich image details or drastic brightness changes, and dividing large-size partitions for regions with flat images or uniform brightness.

[0015] Optionally, the determination of the halo risk area includes:

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

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

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

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

[0020] Calculating the change rate of the brightness value sequence through 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 partitions, and the width of the compensation area is larger when the brightness difference is greater.

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

[0023] For the area boundary where the brightness difference exceeds the preset threshold, multiple 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, based on the brightness gradient distribution to generate a transition compensation area and adjust the brightness distribution of the backlight at the boundary, including:

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

[0027] Among them, the compensation amount is allocated according to the proportion of the gradient value, and the larger the gradient value, the higher the corresponding compensation amount.

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

[0029] Performing brightness mapping calculation on the dimming area;

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

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

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

[0033] Compensate and adjust the backlight brightness of the boundary region based on the calculated result of the brightness mapping.

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

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

[0036] Among them, the compensation range of the fast-moving region is larger than that of the static region.

[0037] The present invention also provides a MiniLED backlight system, including a processor and a memory. The memory stores a computer program. When the processor executes the program, it implements the halo suppression method based on edge compensation as described in any one of the above, including:

[0038] A detection unit for detecting the brightness difference boundary between adjacent dimming regions and determining the halo risk region;

[0039] A calculation unit for calculating the brightness gradient distribution of the halo risk region;

[0040] An execution unit for generating a transition compensation region based on the brightness gradient distribution and adjusting the brightness distribution of the backlight at the boundary.

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

[0042] A halo suppression method and a backlight system based on edge compensation provided by the present invention generate a transition compensation region by calculating the brightness gradient distribution, which is beneficial to adjusting the brightness distribution of the backlight at the boundary, realizing natural brightness transition, effectively eliminating the edge halo effect caused by the brightness difference between adjacent dimming regions, and significantly improving the image clarity and contrast.

[0043] The present invention has other characteristics and advantages, which will be obvious from the accompanying drawings incorporated herein and the subsequent specific embodiments, or will be described in detail in the accompanying drawings incorporated herein and the subsequent specific embodiments. These accompanying drawings and specific embodiments are used together to explain the specific principles of the present invention. Description of the Drawings

[0044] 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 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, without creative efforts, other drawings can be obtained based on these drawings.

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

[0046] Figure 2 It 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 It is a flowchart of step S4 in a halo suppression method based on edge compensation provided by an embodiment of the present invention;

[0048] Figure 4 It 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 implementation manners

[0050] To describe in detail the possible application scenarios, technical principles, specific implementable solutions, achievable purposes and effects of the present application, the following will be described in detail with reference to the specific examples 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.

[0051] Referring to "embodiment" herein means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present 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 the present application, as long as there is no technical contradiction or conflict, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

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

[0053] In the description of the present application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that there can be three relationships. 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 " / " herein generally represents an "or" logical relationship between the associated objects before and after.

[0054] 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-secondary, or sequential relationship between these entities or operations.

[0055] Without further limitations, in this application, the expressions "comprising", "including", "having", or other similar expressions used in a statement are intended to cover non-exclusive inclusion. These expressions do not exclude the possibility that there may be additional elements in the process, method, or product that includes the said elements. Thus, in a process, method, or product that includes a series of elements, it can include not only those defined elements, but also other elements not explicitly listed, or elements inherent to such a process, method, or product.

[0056] Similar to the understanding in the "Examination Guidelines", in this application, expressions such as "greater than", "less than", "exceeding", etc. are understood not to include the number itself; expressions such as "above", "below", "within", etc. are understood to include the number itself. In addition, in the description of the embodiments of this application, the meaning of "multiple" is two or more (including two). Similar expressions related to "many", such as "multiple groups", "multiple times", etc., are understood in the same way, unless otherwise specifically defined.

[0057] In the description of the embodiments of this application, spatial-related expressions such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "perpendicular", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate the orientation or positional relationship based on the specific embodiment or the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the specific embodiments of this application or facilitating the understanding of the reader, and does 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 construed as a limitation to the embodiments of this application.

[0058] Unless otherwise clearly specified or limited, in the description of the embodiments of this application, expressions such as "installed", "connected", "joined", "fixed", "set", etc. should be understood in a broad sense. For example, the said "connection" can be a fixed connection, a detachable connection, or an integral setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be directly connected, or indirectly connected through an intermediate medium; it can be the internal communication of two components or the interaction relationship between two components. For those skilled in the art to which this application pertains, the specific meanings of the above expressions in the embodiments of this application can be understood according to specific circumstances.

[0059] Please refer toFigure 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 comprises:

[0065] S11. Feature extraction.

[0066] In this embodiment, the feature extraction of this step is performed in the following manner:

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

[0068] Specifically, it includes:

[0069] Use histogram statistics, brightness variance / standard deviation, and dynamic range to analyze brightness distribution and contrast; use edge detection algorithms such as Sobel and Canny to extract image edges, count edge density, and use gray-level co-occurrence matrix (GLCM) or Gabor filter to quantify texture to analyze spatial details; analyze frequency domain features through Fourier transform; in video scenes, use optical flow method or frame difference method to calculate the change amplitude of adjacent frames to analyze motion characteristics; calculate the brightness difference of adjacent dimming areas to analyze regional correlation. Calculate the brightness histogram of the image and judge the complexity based on whether its distribution is concentrated or dispersed. If the distribution is concentrated, such as the presence of large blocks of pure black or pure white areas, it indicates low complexity; if the distribution is dispersed and presents a state of light and dark interlacing, the complexity is high. At the same time, calculate the brightness variance or standard deviation. 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.

[0070] 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. Use gray-level co-occurrence matrix (GLCM) or Gabor filter to quantify texture complexity. High-frequency details such as leaves and hair will significantly increase the complexity.

[0071] In terms of frequency domain analysis, with the help of Fourier transform, for an image with a high proportion of high-frequency components, it has rich details and higher complexity; for an image dominated by low-frequency components, it has more smooth regions and lower complexity.

[0072] Motion feature analysis is generally used in video scenarios. By using the optical flow method or frame difference method to calculate the change amplitude between adjacent frames, a dynamic scene requires a higher dimming frequency and also has higher complexity.

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

[0074] S12. Comprehensive scoring.

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

[0076] Exemplarily, the quantitative scoring method adopted 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 a classification decision.

[0079] According to the preset threshold to divide levels (for example: 0 - 30 is low, 30 - 70 is medium, 70 - 100 is high), the image content is divided into low complexity (Level1), medium complexity (Level2), and high complexity (Level3).

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

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

[0082] By calculating features such as the gradient and entropy of the image, combined with the classification result of the image content complexity, the boundary of each region is determined to achieve reasonable division of the image.

[0083] When the image content has high complexity and large brightness change, smaller dimming regions are divided to achieve fine dimming; when the image features indicate a rough dimming control requirement, that is, the image content has low complexity and small brightness change, larger dimming regions are divided to reduce the computational complexity. Specifically, the boundary of each region is determined by calculating features such as the gradient and entropy of the image.

[0084] Specifically, the scenarios corresponding to the three complexities are as follows:

[0085] Low-complexity scenario: It has a large area of single brightness, low dynamic range, and / or sparse edge features. In this case, a larger dimming area is adopted to reduce the dimming frequency and lower the backlight power consumption.

[0086] Medium-complexity scenario: It has features such as medium dynamic range, local light and dark alternation, and a small amount of details. A medium-sized dimming area is adopted, and the frequency is dynamically adjusted to balance image quality and power consumption.

[0087] High-complexity scenario: It has characteristics such as high dynamic range (HDR), dense edges, dense textures, and rapid changes in light and dark. A fine-grained dimming area is adopted, and high-frequency dimming is combined with a halo suppression algorithm to further improve the display effect.

[0088] In this embodiment, for different complexity scenarios, the following dimming methods are adopted:

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

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

[0091] For the high-complexity scenario, a small dimming area is adopted to increase the dimming frequency.

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

[0093] 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 a 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 backlight brightness increase amplitude is limited to achieve precise control of the brightness of different areas. For example, for dark area pixels, the backlight brightness gain is appropriately increased, and the gain coefficient is set between 1.2 - 1.5 to show the details in the dark area; for bright area pixels, the backlight brightness increase amplitude is limited, and the gain coefficient is set between 0.8 - 1.0 to ensure the color saturation and details in the bright area.

[0094] S4. Halo suppression processing.

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

[0096] S41. Detect the brightness difference boundary between adjacent dimming areas and determine the halo risk area.

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

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

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

[0100] Specifically, in this step, a sequence of brightness values is sampled along the boundary normal direction, for example, sampling every 1 pixel, and then the change rate 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 visually reflect the trend and degree of light change in this area.

[0101] S43. Generate a transition compensation area based on the brightness gradient distribution and adjust the brightness distribution of the backlight 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 where the brightness gradient change rate is greater than the 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 ratio of the gradient values. If the gradient value of a certain pixel is 1 and the gradient value of another pixel is 2, then 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 partitions. For example, if the brightness difference between adjacent partitions is 30, the width of the corresponding compensation area is set to 8 pixels; if the brightness difference increases to 50, the width of the compensation area increases to 12 pixels accordingly. And at the boundary of the area where the brightness difference exceeds the preset threshold, multiple layers of gradient compensation bands are set along the brightness gradient direction. From the center of the boundary to both sides, the brightness adjustment amount of each layer of gradient compensation band decreases layer by layer to achieve a natural transition.

[0105] S5. Dynamically adjust the parameters of the transition compensation area in the video scene.

[0106] This step is applicable to the video scene.

[0107] In the video scene, the system dynamically adjusts the parameters of the transition compensation area according to the motion characteristics of the video content. When it detects that an object in the picture is moving rapidly, such as the high-speed running scene of athletes in a sports event, the compensation range of the rapidly moving area will increase accordingly; while for static areas, such as the background buildings in the video, the compensation range is relatively small.

[0108] For example:

[0109] In a high - complexity scenario, after completing the brightness mapping calculation, halo suppression processing is performed on the edge pixels of the dimming area. According to the edge compensation algorithm, the brightness values are sampled at 1 - pixel intervals along the boundary normal direction, and a brightness gradient distribution map is generated through differential operations. When the brightness gradient change rate is greater than a preset value (such as 0.5), a compensation area is delimited, and the compensation amount is distributed proportionally according to the gradient value. For example, the compensation amount of a pixel with a gradient value of 2 is twice that of a pixel with a gradient value of 1. For the area boundary where the brightness difference exceeds the threshold (such as 30), multiple layers of gradient compensation bands are set along the brightness gradient direction, and the brightness adjustment amount gradually decreases from the center of the boundary to both sides to suppress the halo.

[0110] In a dynamically - dimming video scenario, the current frame is first divided into blocks according to common specifications such as 2X2, 4×4, etc. During motion estimation, in the reference frame (forward, backward, or bidirectional), algorithms such as full - search are used to find the matching block, and the matching degree is evaluated according to the sum of absolute differences criterion to determine the motion vector. Information is obtained from the reference frame according to the motion vector to calculate the predicted value of the current block, and a predicted frame is generated by combination. By comparing the predicted frame with the actual current frame, combined with the MiniLED backlight zoning, the brightness adjustment strategy for the dimming area is determined. If the difference is small, the brightness is reduced for energy conservation; if the difference is large, the brightness is maintained or increased to ensure image quality, reducing ghosting and blurring and improving the display quality of dynamic images.

[0111] Please refer to Figure 4 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 halo suppression method is implemented, including:

[0112] A detection unit 10, configured to detect the brightness difference boundary between adjacent dimming areas and determine the halo - risk area;

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

[0114] An execution unit 30, 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, each unit operates collaboratively to implement the halo suppression function, including:

[0116] A detection unit 10, configured 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 simultaneously perform color - space conversion.

[0117] Specifically, the detection unit 10 analyzes from two dimensions: the complexity of the image content and the characteristics of the brightness distribution. The brightness distribution and contrast are analyzed through histogram statistics, brightness variance / standard deviation, and dynamic range; the edge density is extracted by using edge detection algorithms such as Sobel and Canny to count the edges of the image, and the spatial details are quantified by using the gray-level co-occurrence matrix (GLCM) or Gabor filter to analyze the texture; the frequency domain characteristics are analyzed by performing Fourier transform; in the video scenario, the optical flow method or frame difference method is used to calculate the change amplitude of adjacent frames to analyze the motion characteristics; the brightness difference between adjacent dimming regions is calculated to analyze the regional correlation.

[0118] A quantization score is generated through a weighted formula (complexity = 0.5 × brightness variance + 0.3 × edge density + 0.2 × dynamic range), and the image content is divided into low complexity (Level1), medium complexity (Level2), and high complexity (Level3) according to a preset threshold (for example: 0 - 30 is low, 30 - 70 is medium, 70 - 100 is high).

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

[0120] By detecting the brightness difference boundary between adjacent dimming regions, if the brightness difference between adjacent dimming regions exceeds the preset brightness difference threshold, then the region is determined as a halo risk region, 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 region boundary, such as sampling every 1 pixel, and then calculating the change rate of the brightness value sequence through differential operation to generate a brightness gradient distribution map.

[0122] The execution unit 30 is used to count the pixel brightness distribution within each dimming region for each dimming region, and combine with the human eye visual characteristic curve to calculate the target brightness value of the MiniLED backlight in this region. Using a non-linear mapping function, the pixel brightness of the image is mapped to the backlight brightness range. For dark area pixels, the backlight brightness gain is appropriately increased. Specifically, the gain coefficient is set between 1.2 - 1.5; for bright area pixels, the increase amplitude of the backlight brightness is limited. Specifically, the gain coefficient is set between 0.8 - 1.0.

[0123] Determine the range and compensation amount of the compensation area according to the brightness gradient distribution map. Pixel areas where the brightness gradient change rate is greater than the preset gradient will be included in the compensation area, and the compensation amount is allocated according to the proportion of the gradient value. If the gradient value of a certain pixel is 1 and the gradient value of another pixel is 2, then the compensation amount of the latter will be twice that of the former.

[0124] The width of the transitional compensation area is positively correlated with the brightness difference between adjacent partitions. If the brightness difference between adjacent partitions is 30, the width of the corresponding compensation area is set to 8 pixels; if the brightness difference increases to 50, the width of the compensation area increases to 12 pixels accordingly. And at the boundary of the area where the brightness difference exceeds the preset threshold, multiple layers of gradient compensation bands are set along the brightness gradient direction. From the center of the boundary to both sides, the brightness adjustment amount of each layer of gradient compensation band decreases layer by layer.

[0125] In the video scenario, dynamically adjust the parameters of the transitional compensation area according to the motion characteristics of the video content. When fast movement of an object in the picture is detected, the compensation range of the fast movement area will increase accordingly; for static areas, the compensation range is relatively small.

[0126] The present invention effectively eliminates the edge halo effect, greatly improves the image clarity, and reduces the edge blurring phenomenon of the picture by 70%. The adaptive zoning and brightness mapping strategy not only improve the contrast but also retain the image details to the greatest extent. The display of details in the dark area is improved by 50%, and the color saturation in the bright area is improved by 30%. In the video scenario, dynamically adjusting the parameters of the transitional compensation area significantly improves the display effect of the dynamic picture, reduces the motion blur phenomenon of the picture by 80%, and brings a better visual experience to users.

[0127] Finally, it should be noted that although the above embodiments have been described in the text and drawings of the specification of the present application, the patent protection scope of the present application cannot be limited thereby. Any technical solutions obtained by equivalent structure or equivalent process substitution or modification based on the essential concept of the present application and using the content recorded in the text and drawings of the specification of the present application, as well as those directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are all included in the patent protection scope of the present application.

Claims

1. A halo suppression method based on edge compensation, characterized in that: include: Detect the brightness difference boundary between adjacent dimming areas and determine the halo risk area; Calculating the brightness gradient distribution of the halo risk area; 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.

2. The method for suppressing halo 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: According to the image gradient characteristics and brightness distribution characteristics, the adaptive grid partitioning algorithm is used to dynamically adjust the partition size; Small-size partitions are used for areas with rich image details or drastic brightness changes, and large-size partitions are used for areas with flat images or uniform brightness.

3. The method for suppressing halo based on edge compensation according to claim 1, characterized in that: 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.

4. The method for suppressing halo based on edge compensation according to claim 1, characterized in that: 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.

5. The method for suppressing halo 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 partitions, and the greater the brightness difference, the greater the width of the compensation area.

6. The method for suppressing halo based on edge compensation according to claim 5, 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.

7. 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.

8. The method for suppressing halo 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 processing performs compensation adjustment on the backlight source brightness of the boundary area based on the brightness mapping calculation result; The step of adjusting the brightness distribution of the backlight source at the boundary further includes: The backlight source brightness of the boundary area is compensated and adjusted based on the brightness mapping calculation result.

9. 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 video content; The compensation range of the fast-moving area is larger than that of the static area.

10. 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 9, including: A detection unit, used to detect brightness difference boundaries between adjacent dimming areas and determine halo risk areas; A calculation unit, used for calculating the brightness gradient distribution of the halo risk area; The execution unit is used to generate a transition compensation area based on the brightness gradient distribution and adjust the brightness distribution of the backlight source at the boundary.

Images