Method and system for optimizing dynamic contrast ratio of liquid crystal display screen

Abstract

The invention relates to the field of liquid crystal display, and discloses a liquid crystal display screen dynamic contrast optimization method and system, and the method comprises the following steps: collecting ambient light intensity and backlight module temperature parameters in real time, and combining with image content entropy analysis to dynamically generate a non-uniform backlight partition grid; a brightness diffusion physical model is constructed based on hardware parameters such as the refractive index of the light guide plate and the LED spacing, and the optimal brightness of each partition is solved by adopting a temperature self-adaptive improved particle swarm algorithm; driving voltage is generated through piecewise nonlinear gamma mapping and inverse function iterative calculation, and the driving voltage is adapted to the nonlinear response characteristics of the backlight module; a hardware accelerator is used for executing a diffusion equation and optimization calculation in parallel, and model parameters are dynamically adjusted in combination with feedback of an optical sensor, so that a closed-loop control link is formed. According to the method, the contrast performance and the dynamic scene adaptability of the display screen are comprehensively improved through dynamic partition adaptive to the environment, an optimization algorithm driven by a physical model and real-time processing accelerated by hardware.

Description

Technical Field

[0001] The present invention relates to the field of liquid crystal display technology, and in particular to a method and system for optimizing the dynamic contrast ratio of a liquid crystal display screen. Background Art

[0002] In existing LCD display technology, the static division method of backlight partitions is difficult to adapt to dynamic scenes and environmental changes. Fixed partition grids are prone to halo effects and brightness distortion in detail areas under complex lighting conditions. Especially when displaying high dynamic range content, it is difficult to balance the coordinated control of local highlights and dark field details.

[0003] Traditional optimization algorithms mostly construct objective functions based on idealized assumptions, and do not fully consider the actual impact of hardware parameters such as the refractive characteristics of the light guide plate and the LED layout on brightness diffusion, resulting in a deviation between the algorithm output and the physical system response, affecting the contrast optimization effect.

[0004] In addition, the global gamma correction strategy cannot adapt to the local brightness characteristics of non-uniform backlight partitions, which can easily cause color distortion in low grayscale areas. The lack of a closed-loop feedback mechanism makes it difficult to cope with dynamic interference such as backlight module aging and environmental changes, resulting in display performance degradation during long-term use.

[0005] Existing technologies have significant limitations in terms of multi-objective optimization real-time, hardware parameter coupling and environmental adaptability, which restricts the realization of high dynamic contrast display effects. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for optimizing the dynamic contrast of a liquid crystal display screen, which solves the technical problems of dynamic scene contrast distortion, significant halo effect, poor environmental adaptability and long-term display performance attenuation caused by fixed backlight partitions, global gamma correction and lack of closed-loop feedback in existing liquid crystal displays.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for optimizing the dynamic contrast ratio of a liquid crystal display screen, comprising the following steps: Collect the brightness component and environmental parameters of the input image; Dynamically generate a non-uniform backlight partition grid based on image content; Establish a backlight brightness collaborative optimization model to solve the optimal brightness value of each partition; generating a nonlinear gamma mapping curve according to the optimal brightness value; Synthesize drive signals and perform closed-loop feedback adjustments.

[0008] Preferably, the step of dynamically generating a non-uniform backlight partition grid based on image content comprises: The local entropy value of each area of ​​the image is calculated by sliding window traversal, and the image is divided into multiple initial partitions according to the preset entropy value threshold, where the high entropy value area corresponds to the detail-rich area; The entropy value threshold is dynamically adjusted according to the ambient light intensity, and adjacent partitions whose areas are smaller than the preset threshold are merged to generate a final non-uniform backlight partition grid.

[0009] Preferably, the specific rules for merging adjacent partitions whose areas are smaller than a preset threshold include: Determine the difference in the mean brightness of adjacent partitions, and merge them if the difference is less than the preset tolerance range; The area of ​​the merged partitions does not exceed the minimum controllable area size of the backlight module.

[0010] Preferably, the step of establishing a backlight brightness collaborative optimization model to solve the optimal brightness value of each partition includes: A brightness diffusion model is built based on the refractive index of the light guide plate of the backlight module and the LED spacing parameters to simulate the brightness diffusion relationship between adjacent partitions; A multi-objective optimization model is constructed, wherein the optimization objectives are to maximize contrast, minimize power consumption, and shorten liquid crystal response time, and a brightness difference constraint between adjacent partitions is introduced.

[0011] Preferably, the method for solving the multi-objective optimization model includes: An improved particle swarm algorithm is used for iterative calculation, and the inertia weight of the algorithm is dynamically adjusted according to the real-time temperature of the backlight module; During the particle update process, the brightness difference between adjacent partitions is constrained not to exceed a preset threshold.

[0012] Preferably, the step of generating a nonlinear gamma mapping curve according to the optimal brightness value comprises: According to the optimal brightness value of each partition, a nonlinear gamma mapping curve is generated for each partition, wherein the gamma mapping curve is obtained by fitting a function that minimizes an error between target brightness and actual brightness; Based on the proportional relationship between the input brightness and the target brightness, the input brightness is mapped to a driving voltage signal.

[0013] Preferably, the method for generating the nonlinear gamma mapping curve includes: The gamma curve of each partition is locally fitted using a piecewise quadratic function, where the segment interval is dynamically divided according to the optimal brightness value of the partition; The input brightness value is converted into a driving voltage through an inverse function calculation.

[0014] Preferably, the step of synthesizing the driving signal and performing closed-loop feedback adjustment includes: The matrix operations of the brightness diffusion model and the iterative calculations of the optimization algorithm are performed in parallel through the hardware accelerator; According to the actual brightness data fed back by the light sensor, the diffusion coefficient of the brightness diffusion model is dynamically adjusted and the weight parameters are optimized.

[0015] Preferably, the hardware accelerator is implemented using FPGA or ASIC, and its architecture includes: A parallel computing unit for processing the brightness diffusion equation of the backlight partition in blocks; The pipeline scheduling module allocates computing resources to partitions with high real-time requirements according to priority.

[0016] The present invention also provides a liquid crystal display screen dynamic contrast optimization system, comprising: A data acquisition module is used to obtain image brightness components and environmental parameters; Dynamic partitioning module, which generates non-uniform backlight partitioning grid based on image entropy value; Collaborative optimization module, which builds brightness diffusion model and solves multi-objective optimization problems; Gamma mapping module, which generates nonlinear gamma curve and synthesizes driving signal; The execution feedback module executes the drive and adjusts the parameters in a closed loop through the hardware accelerator.

[0017] The present invention provides a method and system for optimizing the dynamic contrast ratio of a liquid crystal display screen. The method has the following beneficial effects: 1. The present invention collects ambient light intensity and temperature parameters in real time, dynamically adjusts the entropy value partition threshold and merging rules, and can realize intelligent adjustment of the spatial resolution of backlight partitions as the ambient light changes, suppress invalid subdivision partitions in strong light environments, and retain the independent control capability of detail areas in weak light environments, effectively avoiding the halo effect and detail loss caused by fixed partitions.

[0018] 2. The present invention combines the refractive index of the light guide plate of the backlight module, the LED spacing and other hardware parameters to construct a brightness diffusion equation, embeds the physical characteristics into the optimization objective function, and dynamically adjusts the algorithm weights through a temperature adaptive mechanism. It can suppress the surge in driving power consumption in a high temperature environment while ensuring high contrast, thereby achieving a balance between display effect and hardware security.

[0019] 3. The present invention generates a locally fitted gamma curve based on the optimal brightness value of the partition, and adopts piecewise quadratic function and Newton iterative inverse operation, which can overcome the modeling error of the traditional global gamma curve for nonlinear response, realize high-precision conversion of input brightness to driving voltage, and significantly reduce color distortion in low grayscale areas.

[0020] 4. The present invention realizes the parallel calculation of the brightness diffusion equation and the optimization algorithm through the FPGA / ASIC architecture, and combines it with the dynamic priority scheduling strategy to meet the real-time processing requirements of high refresh rate scenes (such as 120Hz game screens) and ensure the frame rate stability of large-size display screens under multi-partition control.

[0021] 5. The present invention corrects the diffusion coefficient and optimizes the weight in real time based on the light sensor data, forming a closed-loop control link from parameter acquisition, model calculation to drive output, which can compensate for the brightness deviation caused by factors such as backlight module aging and environmental mutation, and improve the consistency of the display system in long-term use. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the system structure of the present invention.

[0023] Among them, 10, data acquisition module; 20, dynamic partitioning module; 30, collaborative optimization module; 40, gamma mapping module; 50, execution feedback module. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Please see attached Figure 1 The present invention provides a method for optimizing the dynamic contrast of a liquid crystal display screen, which improves the contrast performance through dynamic partition collaborative optimization and nonlinear mapping technology.

[0026] like Figure 1 As shown, the method for optimizing the dynamic contrast ratio of a liquid crystal display screen may include the following steps: S1, collecting the brightness component and environmental parameters of the input image; S2, dynamically generate a non-uniform backlight partition grid based on image content; S3, establishing a backlight brightness collaborative optimization model to solve the optimal brightness value of each partition; S4, generating a nonlinear gamma mapping curve according to the optimal brightness value; S5, synthesize the driving signal and perform closed-loop feedback adjustment.

[0027] The following is a detailed description of each step in the method of the present invention, which comprehensively describes the specific implementation principle, technical details and process of each step.

[0028] For step S1, the specific implementation process is as follows: First, a calculation model for the brightness component of the input image is established. The brightness component is converted through the CIE 1931 standard colorimetry formula. The specific calculation relationship is:

[0029] In the formula, , , Represents the pixel coordinates of the input image The red, green, and blue channel values ​​at is the converted brightness component. This brightness component is used to characterize the light and dark distribution characteristics of each area in the image and provide a data basis for subsequent dynamic partitioning.

[0030] Then the ambient light intensity parameters are collected through the light sensor , exemplarily, the light sensor is integrated at the border of the display screen, and its spectral response range covers the visible light band. The measured value of the ambient light intensity is transmitted to the main control unit through the analog-to-digital conversion module for dynamically adjusting the sensitivity threshold of subsequent partitions.

[0031] Furthermore, the real-time temperature parameters of the backlight module are obtained through the temperature sensor. The temperature sensor is mounted on the heat sensitive area of ​​the backlight driving circuit. The temperature parameter is dynamically bound to the weight coefficient in the optimization algorithm. The specific binding relationship is:

[0032] In the formula, is the base weight coefficient, is the reference temperature value. This relationship can achieve real-time adaptation of algorithm parameters and hardware status.

[0033] The brightness component , Ambient light intensity And temperature parameters It is transmitted to the dynamic partitioning module through the data bus and constitutes the input condition for the subsequent steps.

[0034] The above steps can realize the standardized extraction of image brightness information, and provide physical constraints for dynamic optimization through multi-source environmental parameter collection to ensure the environmental adaptability of subsequent partitions.

[0035] For step S2, the specific implementation process is as follows: First, a dynamic partition generation model is established based on the input brightness component Calculate the local entropy value. The specific formula is:

[0036] In the formula, Represents the gray level in the sliding window (exemplarily using an 8×8 pixel window) The probability of occurrence, For coordinates The local entropy value at . The high entropy value area corresponds to the detailed area with dense texture or edge in the image, and the low entropy value area corresponds to the smooth or uniform area. This calculation can quantify the detail complexity of the image area and provide a basis for partitioning.

[0037] Then, the ambient light intensity parameter Dynamically adjust the entropy threshold, the adjustment formula is:

[0038] In the formula, is the baseline entropy threshold (the exemplary value is 4.0), is the reference ambient light intensity (the exemplary value is 100 lux). When the ambient light increases, the threshold increases proportionally to reduce the over-segmentation caused by external light interference.

[0039] Furthermore, a merge operation is performed on the initial partitions, and the merge rule has the following constraints: 1. Brightness mean difference constraint: If the average brightness difference between adjacent partitions satisfies

[0040] Then the merge is triggered. In the formula, , is the average brightness of adjacent partitions, An exemplary setting is 10% of the brightness dynamic range (0-255), that is, 25.5.

[0041] 2. Minimum controllable area constraint: The area of ​​the merged partitions must meet

[0042] In the formula The minimum controllable area size of the backlight module is 16×16 pixels, which is determined by the physical addressing capability of the backlight driver chip (such as the resolution of the LED control unit).

[0043] After the dynamic partition grid is generated, it serves as the spatial constraint basis for the subsequent brightness collaborative optimization.

[0044] For step S3, the specific implementation process is as follows: First, a physical model for collaborative optimization of backlight brightness is constructed based on the hardware parameters of the backlight module (refractive index of the light guide plate , LED spacing and wavelength of light source ) Establish a brightness diffusion equation to quantify the brightness coupling relationship between adjacent partitions. The mathematical form of the equation is:

[0045] Where: is the optical diffusion coefficient, and its value is directly related to the light guide material, LED layout and light source characteristics; is the Laplace operator of brightness distribution, simulating the diffusion effect of brightness in the spatial dimension; is the liquid crystal molecule deflection voltage response function (Sigmoid function); its derivative Reflects the nonlinear effect of voltage change on the deflection rate of liquid crystal molecules; is the gain factor, used to adjust the input brightness The driving strength of the backlight brightness. This equation quantifies the brightness diffusion coupling relationship between adjacent partitions.

[0046] Then a multi-objective optimization problem is established to mathematically express the trade-off between contrast, power consumption and response time. The objective function is defined as:

[0047] Where: , represents the mean square error between the target brightness and the actual brightness; is the power consumption of the driving circuit, which is proportional to the backlight brightness and the square of the driving voltage; is the liquid crystal response time, which is determined by the rate of change of the driving voltage and the material properties; , , It is a dynamic weight coefficient, which is adjusted in real time through temperature sensor data.

[0048] Further define constraints to ensure that the optimization results meet the requirements of physical feasibility and visual smoothness: 1. Minimum brightness constraint:

[0049] in For partition The maximum drivable brightness is determined by the hardware specifications of the backlight module.

[0050] 2. Constraints on brightness differences between adjacent partitions:

[0051] In the formula Represents a partition The adjacent partition set of The synergistic effect suppresses the halo effect.

[0052] Finally, the improved particle swarm algorithm is used to solve the optimization problem. The steps are as follows: Step 1: Particle swarm initialization Define the particle position vector , indicating the Particle pairs For example, the particle swarm size is set to 50, and the initial position of each particle is randomly generated within the allowed range of the partition brightness:

[0053] In the formula is a random number uniformly distributed between [0,1], , determined by the minimum brightness constraint.

[0054] The generation range of the initial particles and the dynamic partition grid of step S2 ensure that the brightness value of each partition complies with the physical limit of its minimum controllable area size.

[0055] Step 2: Fitness calculation The fitness value of each particle is calculated according to the aforementioned objective function:

[0056] The weight coefficient is calculated according to the formula Real-time adjustment, including temperature parameters Acquired by the sensor in step S1.

[0057] Apply a penalty term to particles that violate the brightness difference constraint:

[0058] In the formula is a penalty coefficient (an exemplary value is 1000), and this constraint is associated with the partition merging rule in step S2.

[0059] Step 3: Individual and group optimal updates Record the historical optimal position of each particle and the global optimal position of the group , the update rule is:

[0060]

[0061] Step 4: Particle velocity and position update Adjust the speed and position according to the improved particle swarm update formula:

[0062]

[0063] Inertia Weight By formula Dynamic adjustment, where is the real-time temperature collected in step S1. When the temperature rises, To suppress particle oscillation and improve convergence stability.

[0064] Speed ​​Item The permissible range of the diffusion coefficient is similar to the above association, in areas with strong diffusion capacity ( Large value) to expand the search range:

[0065] In the formula For partition The diffusion coefficient is determined by the refractive index of the light guide plate and LED spacing Calculated.

[0066] Step 5: Constraint Enforcement The following mandatory corrections are performed on the updated particle positions: 1. Boundary constraints: like , then let ; like , then let .

[0067] 2. Brightness difference constraint: For each partition , traverse its adjacent partitions ,like , then scale the brightness value proportionally:

[0068] Adjacent Partition Set The dynamic partition grid generated in step S2 ensures that the constraints are consistent with the spatial relationship of the physical partitions.

[0069] Step 6: Termination condition judgment Repeat steps 2-5 until any of the following termination conditions is met: The optimal fitness value of the group changes by less than 1% in 20 consecutive iterations; The total number of iterations reaches a preset upper limit (set to 200 times in an exemplary manner).

[0070] When the temperature When the convergence threshold is lowered (for example, from 1% to 2%), the algorithm can be prevented from falling into the local optimum under high temperature environment.

[0071] Step 7: Output optimization results The global optimal position As the optimal brightness value for each partition Output and passed to the gamma mapping module.

[0072] This implementation achieves global optimization of backlight brightness distribution through deep coupling of physical models and optimization algorithms.

[0073] For step S4, the specific implementation process is as follows: First, based on the optimal brightness value of each partition output in step S3 , a nonlinear gamma mapping curve is generated for each partition. The curve is obtained by fitting the error function that minimizes the target brightness and the actual brightness. The error function is defined as:

[0074] In the formula, is the driving voltage sampling point (the exemplary value is 0 to 5V, with a step length of 0.1V), For partition The gamma curve function, For voltage The corresponding measured brightness value.

[0075] Furthermore, a piecewise quadratic function is used to locally fit the gamma curve to balance the computational complexity and fitting accuracy. The piecewise function is in the form of:

[0076] Segment interval According to the optimal brightness value Dynamic division, the specific rules are: Will The corresponding voltage range is divided into sub-intervals (for example ), and each subinterval is fitted with a quadratic function independently.

[0077] Then the input brightness is calculated by the inverse function Converted into driving voltage. For each pixel , according to the partition Gamma curve solution:

[0078] In the formula, Represents the inverse function of the gamma curve, which is solved by Newton iteration method. The iteration formula is:

[0079] Iteration initial value Set to the driving voltage value of the corresponding partition in the previous frame to improve calculation efficiency.

[0080] For step S5, the specific implementation process is as follows: First, the matrix operation of the brightness diffusion model established in step S3 and the iterative calculation of the optimization algorithm are performed in parallel by a hardware accelerator. The hardware accelerator can be implemented by FPGA or ASIC. The hardware accelerator is exemplarily implemented by FPGA, and its architecture includes a parallel computing unit and a pipeline scheduling module.

[0081] The parallel computing unit processes the brightness diffusion equation of the backlight partition in blocks, and specifically implements the finite difference method discretization of the brightness diffusion model:

[0082] In the formula, is the spatial step length, is the time step, The liquid crystal response derivative data pre-stored in the lookup table is acquired in real time.

[0083] Furthermore, the pipeline scheduling module dynamically allocates computing resources according to the real-time requirements of the partitions. For example, the high refresh rate area (such as the center area of ​​the game screen) is given the highest priority, and its scheduling strategy satisfies:

[0084] in is the temporal change rate of the partition target brightness, is the diffusion coefficient defined in the brightness diffusion model.

[0085] Then according to the actual brightness data fed back by the light sensor , dynamically adjust the diffusion coefficient of the brightness diffusion model and optimize the weight parameters. The adjustment formula is:

[0086]

[0087] In the formula is the total number of partitions, is the maximum brightness of the backlight module, and Denote L1 and L2 norms respectively. The adjustment is performed in real time by an interrupt service routine of the hardware accelerator.

[0088] The drive signal is synthesized and output to the backlight drive circuit through the LVDS interface, and the adjusted parameters are written back to the optimization model of step S3 to form a closed-loop control link.

[0089] In general, the present invention first dynamically generates a non-uniform backlight partition grid based on the brightness characteristics of the input image and the environmental parameters. The partition rule ensures independent control of the detail area through entropy thresholds and brightness difference constraints; then, a brightness diffusion model is established in combination with the physical characteristics of the backlight module, and a temperature-adaptive multi-objective optimization algorithm is used to solve the optimal brightness of each partition. This process is solved in real time through a particle swarm algorithm and dynamic binding of hardware parameters; then, a nonlinear gamma mapping curve is generated for each partition, and the brightness signal is accurately mapped to a driving voltage through an inverse function conversion; finally, the computing tasks are executed in parallel through a hardware accelerator, and the diffusion coefficient and optimization weight are dynamically adjusted based on the feedback data from the light sensor, forming a complete control link from parameter acquisition, model optimization to drive output, and cross-layer collaboration is achieved between each step through real-time data flow and parameter write-back mechanism to ensure the physical adaptability of brightness control and the real-time responsiveness of dynamic scenes.

[0090] The liquid crystal display screen dynamic contrast optimization system described below and the liquid crystal display screen dynamic contrast optimization method described above can be referred to each other.

[0091] Please see attached Figure 2 The present invention also provides a liquid crystal display screen dynamic contrast optimization system, comprising: The data acquisition module 10 is used to obtain image brightness components and environmental parameters; A dynamic partitioning module 20 generates a non-uniform backlight partitioning grid based on the image entropy value; Collaborative optimization module 30, establishing a brightness diffusion model and solving multi-objective optimization problems; A gamma mapping module 40, generating a nonlinear gamma curve and synthesizing a driving signal; The execution feedback module 50 executes driving and close-loop adjustment of parameters through the hardware accelerator.

[0092] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0093] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing dynamic contrast of a liquid crystal display screen, characterized in that: The following steps are involved: Collect the brightness component and environmental parameters of the input image; Dynamically generate a non-uniform backlight partition grid based on image content; Establish a backlight brightness collaborative optimization model to solve the optimal brightness value of each partition; generating a nonlinear gamma mapping curve according to the optimal brightness value; Synthesize drive signals and perform closed-loop feedback adjustments.

2. The method for optimizing dynamic contrast ratio of a liquid crystal display according to claim 1, characterized in that: The step of dynamically generating a non-uniform backlight partition grid based on image content comprises: The local entropy value of each area of ​​the image is calculated by sliding window traversal, and the image is divided into multiple initial partitions according to the preset entropy value threshold, where the high entropy value area corresponds to the detail-rich area; The entropy value threshold is dynamically adjusted according to the ambient light intensity, and adjacent partitions whose areas are smaller than the preset threshold are merged to generate a final non-uniform backlight partition grid.

3. The method for optimizing dynamic contrast ratio of a liquid crystal display screen according to claim 2, characterized in that: The specific rules for merging adjacent partitions whose areas are smaller than a preset threshold include: Determine the difference in the mean brightness of adjacent partitions, and merge them if the difference is less than the preset tolerance range; The area of ​​the merged partitions does not exceed the minimum controllable area size of the backlight module.

4. The method for optimizing dynamic contrast ratio of a liquid crystal display screen according to claim 1, characterized in that: The step of establishing a backlight brightness collaborative optimization model and solving the optimal brightness value of each partition includes: A brightness diffusion model is built based on the refractive index of the light guide plate of the backlight module and the LED spacing parameters to simulate the brightness diffusion relationship between adjacent partitions; A multi-objective optimization model is constructed, wherein the multi-objective optimization model takes maximizing contrast, minimizing power consumption and shortening liquid crystal response time as optimization objectives, and introduces brightness difference constraints between adjacent partitions.

5. The method for optimizing dynamic contrast ratio of a liquid crystal display screen according to claim 4, characterized in that: The method for solving the multi-objective optimization model includes: An improved particle swarm algorithm is used for iterative calculation, and the inertia weight of the algorithm is dynamically adjusted according to the real-time temperature of the backlight module; During the particle update process, the brightness difference between adjacent partitions is constrained not to exceed a preset threshold.

6. The method for optimizing dynamic contrast ratio of a liquid crystal display screen according to claim 1, characterized in that: The step of generating a nonlinear gamma mapping curve according to the optimal brightness value comprises: According to the optimal brightness value of each partition, a nonlinear gamma mapping curve is generated for each partition, wherein the gamma mapping curve is obtained by fitting a function that minimizes an error between target brightness and actual brightness; Based on the proportional relationship between the input brightness and the target brightness, the input brightness is mapped to a driving voltage signal.

7. The method for optimizing dynamic contrast ratio of a liquid crystal display screen according to claim 6, characterized in that: The method for generating the nonlinear gamma mapping curve comprises: The gamma curve of each partition is locally fitted using a piecewise quadratic function, where the segment interval is dynamically divided according to the optimal brightness value of the partition; The input brightness value is converted into a driving voltage through an inverse function calculation.

8. The method for optimizing dynamic contrast ratio of a liquid crystal display screen according to claim 1, characterized in that: The steps of synthesizing the driving signal and performing closed-loop feedback adjustment include: The matrix operations of the brightness diffusion model and the iterative calculations of the optimization algorithm are performed in parallel through the hardware accelerator; According to the actual brightness data fed back by the light sensor, the diffusion coefficient of the brightness diffusion model is dynamically adjusted and the weight parameters are optimized.

9. The method for optimizing dynamic contrast ratio of a liquid crystal display screen according to claim 8, characterized in that: The hardware accelerator is implemented using FPGA or ASIC, and its architecture includes: A parallel computing unit for processing the brightness diffusion equation of the backlight partition in blocks; The pipeline scheduling module allocates computing resources to partitions with high real-time requirements according to priority.

10. A liquid crystal display screen dynamic contrast optimization system, used to execute the method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to obtain image brightness components and environmental parameters; Dynamic partitioning module, which generates non-uniform backlight partitioning grid based on image entropy value; Collaborative optimization module, which builds brightness diffusion model and solves multi-objective optimization problems; Gamma mapping module, which generates nonlinear gamma curve and synthesizes driving signal; The execution feedback module executes the drive and adjusts the parameters in a closed loop through the hardware accelerator.

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