CN111540312A - Gamma modulation method - Google Patents
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- CN111540312A CN111540312A CN202010645191.2A CN202010645191A CN111540312A CN 111540312 A CN111540312 A CN 111540312A CN 202010645191 A CN202010645191 A CN 202010645191A CN 111540312 A CN111540312 A CN 111540312A
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
- G09G3/22—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources
- G09G3/30—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels
- G09G3/32—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels semiconductive, e.g. using light-emitting diodes [LED]
- G09G3/3208—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels semiconductive, e.g. using light-emitting diodes [LED] organic, e.g. using organic light-emitting diodes [OLED]
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G2320/00—Control of display operating conditions
- G09G2320/02—Improving the quality of display appearance
- G09G2320/0271—Adjustment of the gradation levels within the range of the gradation scale, e.g. by redistribution or clipping
- G09G2320/0276—Adjustment of the gradation levels within the range of the gradation scale, e.g. by redistribution or clipping for the purpose of adaptation to the characteristics of a display device, i.e. gamma correction
Abstract
The invention discloses a Gamma modulation method, which utilizes the Gamma modulation quantity of a panel to be modulated and the target value of the Gamma modulation quantity to construct an unconstrained optimization problem about the Gamma modulation quantity and determine an integral optimization target; obtaining a conservative optimal solution of the first-order gradient of the Gamma modulation quantity following conservative change through a difference relation; and correcting the conservative optimal solution according to a preset algorithm to obtain a first-order gradient and a second-order Hessian approximate value of the overall optimization target, and calculating the next iterative modulation step length by using the approximate value to achieve the aim of accelerating Gamma modulation.
Description
Technical Field
The invention belongs to the field of Gamma modulation of display panels, and particularly relates to a Gamma modulation method.
Background
On the OLED line, Gamma modulation is an iterative optimization technique for making the panel chromaticity and luminance approach the target values by changing the module register values (or voltage values). The aim is to coordinate the real linear response of the module with the nonlinear response under the perception of human eyes, so as to achieve the luminous effect of natural transition and distinct hierarchy. In the Gamma modulation process, the display requirements must be met for all the bindings specified by the customer. For this purpose, RGB register values corresponding to convergence of the tie point chromaticity and luminance xyLv must be obtained. In each binding point Gamma modulation process, after initial values of RGB registers are given, a set of xyLv data of the panel is measured by using a colorimeter. If the precision of the xyz reaches the display requirement, the binding point is converged and the adjustment is completed. Otherwise, calculating the iteration step length according to the optimization algorithm, adjusting the RGB register value, and repeating the measurement and convergence judgment process.
However, the convergence speed of the Gamma modulation method depends on the convergence order of the optimization algorithm itself on one hand and is also related to the response characteristic of the panel on the other hand. The optimization algorithm with higher convergence order often needs to depict more refined high-order optimization information. Because the calculation of the optimization information depends on the actual response of the panel, the instability of the panel chromaticity and brightness response and the self measurement error of the probe in the Gamma adjustment process make the acquisition of accurate optimization information difficult, thereby restricting the concrete performance of the Gamma modulation method.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a Gamma modulation method, aiming at solving the technical problem of instability of iteration caused by data errors in the iteration process and achieving the purpose of improving the Gamma modulation precision.
To achieve the above object, according to an aspect of the present invention, there is provided a Gamma modulation method, including:
updating the register value of the panel to be modulated by using the modulation step length, and repeating the steps until the Gamma modulation quantity of the panel to be modulated meets a preset Gamma adjustment condition, wherein the current modulation step length is the difference value between the current register value and the last register value;
the obtaining process of the modulation step length is as follows:
constructing an unconstrained optimization function related to the Gamma modulation quantity, wherein the unconstrained optimization function is the square or absolute value of the relative error between the minimized Gamma modulation quantity and the Gamma modulation quantity target value;
obtaining a conservative optimal solution of the Gamma modulation quantity following conservative change about the first-order gradient of the register value, wherein the conservative optimal solution follows the L2 norm of the difference value of the minimized first-order gradient and the appointed first-order gradient of the Gamma modulation quantity of a certain iteration;
and acquiring the next modulation step by using the conservative optimal solution.
As a further improvement of the present invention, when the number of the Gamma modulation amounts is plural, weight parameters corresponding to the plural Gamma modulation amounts one by one are set, and a weighted sum of the squares or absolute values of the relative errors between the minimized Gamma modulation amounts and the target value of the Gamma modulation amounts is used as an unconstrained optimization function.
As a further improvement of the invention, the initial value of the first order gradient of the Gamma modulation quantity is obtained by random giving or fitting acquired data or pre-adjusting, and the initial value of the register value is obtained by random giving or fitting acquired data.
As a further improvement of the present invention, the conservative optimal solution further satisfies a preset difference relationship, where the preset difference relationship is:
and constructing a trapezoid difference grid or Euler backward difference format by utilizing the first-order gradient and the first-order gradient of a specified iteration, the modulation difference value and the modulation step length.
As a further improvement of the present invention, the obtaining of the next modulation step by using the conservative optimal solution specifically includes: correcting the conservative optimal solution according to a predetermined algorithm, combining an unconstrained optimization function to obtain a Gamma modulation step length,
wherein the predetermined algorithm comprises: a sliding window with the size of n is preset, conservative optimal solutions are stored inside the sliding window, each conservative optimal solution is a conservative optimal solution of the current iteration data and certain iteration data in the last n times, vector median filtering is carried out on the conservative optimal solution in the sliding window, and the Gamma modulation quantity 1-time correction first-order gradient is obtained.
As a further improvement of the present invention, the predetermined algorithm further comprises:
and combining the Gamma modulation quantity first-order gradient corrected for 1 time with the last iteration Gamma modulation quantity first-order gradient according to a preset weight convex to obtain the Gamma modulation quantity first-order gradient corrected for 2 times.
As a further improvement of the present invention, obtaining the next modulation step using the conservative optimal solution includes:
and obtaining an optimization problem objective function first-order gradient approximate value by utilizing the Gamma modulation first-order gradient, and obtaining the next modulation step by utilizing a step expression of a gradient descent algorithm.
As a further improvement of the invention, a second-order Hessian approximate value of the optimization problem target function is obtained by utilizing the first-order gradient approximate value of the optimization problem target function through a BFGS quasi-Newton method iteration format, and the next modulation step length is obtained by utilizing the first-order gradient approximate value of the optimization problem target function and the second-order Hessian approximate value according to a BFGS quasi-Newton method or a gradient descent algorithm step length formula.
As a further improvement of the invention, when the Gamma modulation amount is not converged and the unconstrained optimization function drop value is smaller than a preset threshold value during each iteration, the step length expression of the gradient descent algorithm is adopted to obtain the next modulation step length, otherwise, the step length expression of the BFGS quasi-Newton method is adopted to obtain the next modulation step length;
the method comprises the steps of initializing a register value of a panel to be modulated, a first-order gradient of a Gamma modulation quantity and a second-order Hessian matrix to obtain an initial value of a modulation step, wherein the initial value of the first-order gradient is obtained by random giving or fitting through data acquisition or pre-adjusting, and the initial value of the second-order Hessian matrix is obtained by approximate calculation of a linear part of the Gamma modulation quantity in a Hessian expression.
To achieve the above object, according to one aspect of the present invention, there is provided a computer-readable medium storing a computer program for execution by a terminal device, the program, when executed on the terminal device, causing the terminal device to perform the steps of the above method.
Generally, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
according to the Gamma modulation method, an optimization function of the Gamma modulation quantity is constructed, a constraint condition is further constructed to obtain an optimal solution of a first-order gradient of the current Gamma modulation quantity, further the first-order gradient of the optimization function is obtained, a next modulation step length is obtained, and through establishing a proper mathematical model and carrying out corresponding iterative optimization, the instability of panel brightness and chromaticity response in the Gamma adjustment process and the measurement error of a probe are overcome, so that accurate optimization information is obtained, and the aim of improving the Gamma modulation precision is fulfilled.
According to the Gamma modulation method, the weight parameters which correspond to a plurality of Gamma modulation quantities one by one are set, and the sum of the optimization functions corresponding to the Gamma modulation quantities multiplied by the corresponding weight parameters is used as the total optimization function, so that more accurate optimization information is obtained, and the precision of the Gamma modulation is further improved.
According to the Gamma modulation method, the next modulation step length is obtained by using the step length expression of the gradient descent algorithm and the quasi-Newton algorithm alternately, the optimization target is switched in multiple targets, so that the search is not interfered by the error of the non-optimization target, and the influence caused by the error is overcome to a great extent, so that the high requirement of the conventional simplified Newton method on the accuracy of the optimization information is avoided, and once the relative error of the brightness and the chromaticity exceeds a certain threshold, the simplified Newton method has serious adverse effect on the convergence of the algorithm.
According to the Gamma modulation method, by means of quadratic programming, median filtering, momentum gradient and the like, the noise influence with complex sources in the Gamma modulation process is overcome, relatively reliable high-order optimization information is obtained, the convergence speed of the Gamma modulation method is improved, the first-order gradient of the Gamma modulation quantity is gradually adjusted to be close to a reasonable value through dynamic updating, the reasonable value is used for initializing the first-order gradient of the Gamma modulation quantity of the next panel, the convergence speed is greatly improved at the initial stage of the algorithm and finally tends to be stable, and the self-adaptive effect is achieved.
Drawings
Fig. 1 is a schematic diagram of a Gamma modulation method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other. The present invention will be described in further detail with reference to specific embodiments.
A Gamma modulation method updates a register value of a panel to be modulated by using a modulation step length, repeats the steps until the Gamma modulation quantity of the panel to be modulated meets a preset Gamma adjustment condition, the current modulation step length is a difference value between the current register value and the last register value, the method determines an integral optimization target by constructing an unconstrained optimization problem about the Gamma modulation quantity, obtains a conservative optimal solution of a first-order gradient of the Gamma modulation quantity following conservative change through a difference relation, and calculates the Gamma modulation step length by combining the optimization problem. Those skilled in the art can know that obtaining the next modulation step by using the first-order partial derivative of the current Gamma modulation amount can be implemented by selecting a corresponding optimization algorithm according to requirements, such as a linear convergence algorithm (e.g., a gradient descent algorithm), a super-linear convergence algorithm (e.g., a quasi-newton algorithm), a least square method, and the like, and of course, other optimization algorithms can be selected according to requirements, which are not limited herein.
The unconstrained optimization problem is as follows: minimizing the square or absolute value of the relative error between the Gamma modulation quantity and the target value of the Gamma modulation quantity, and following conservative change as the L2 norm of the difference between the minimized first-order gradient and the first-order gradient of the Gamma modulation quantity of a specified iteration.
Optionally, according to a preset difference relationship, the L2 norm of the difference between the Gamma modulation first-order gradient and the Gamma modulation first-order gradient of a given iteration is minimized, and a conservative optimal solution of the Gamma modulation first-order gradient is obtained.
Optionally, the initial value of the first-order gradient of the Gamma modulation amount is obtained by random giving or fitting through collected data or pre-adjusting, the initial value of the register value is obtained by random giving or fitting through collected data, and the initial value of the modulation step length is obtained through the first-order gradient of the Gamma modulation amount.
Optionally, the conservative optimal solution further satisfies a preset difference relationship, where the preset difference relationship is:
and constructing a trapezoid difference grid or Euler backward difference format by utilizing the first-order gradient and the first-order gradient of a specified iteration, the modulation difference value and the modulation step length.
Optionally, in order to further correct the conservative optimal solution, the first-order gradient conservative optimal solution of the Gamma modulation amount may be first corrected to obtain 1-time corrected first-order gradient of the Gamma modulation amount; specifically, in the step of first correcting the conservative optimal solution of the first-order gradient of the Gamma modulation amount, the preset value isnThe sliding window internally stores Gamma modulation quantity first-order gradient conservative optimal solutions, and each conservative optimal solution is the current iteration data and the latest iteration datanAnd carrying out vector median filtering on the conservative optimal solution of certain iteration data in the next time to obtain 1-time modified first-order gradient of the Gamma modulation quantity. As an example, for a size ofnThe sliding window of (1), the set of internal vectors being
，
Wherein, in the step (A),
respectively are the kth optimal solution, the k-1 optimal solution and the k-n optimal solution in the optimal solution sequence. Due to the instability of panel response and the inherent error of the measuring equipment, the acquisition of the optimal solution is not completely reliable, especially in low light, the increase of relative error can cause serious influence on the calculation of approximate gradient, in order to better inhibit the noise influence, a sliding window is established here, and the traditional vector median filtering method is adoptedThe optimal solution is filtered accordingly to obtain a more reliable first order gradient.
Further, the Gamma modulation amount 1 time of correction of the first order gradient may be corrected again to obtain the Gamma modulation amount 2 times of correction of the first order gradient, and specifically, in the step of correcting the Gamma modulation amount 1 time of correction of the first order gradient again, the Gamma modulation amount 1 time of correction of the first order gradient and the last iteration Gamma modulation amount first order gradient are combined convexly according to the preset weight to obtain the Gamma modulation amount 2 times of correction of the first order gradient. Because the median filtering overcomes the response error of the iteration from the (k-1) th step to the (k-n) th step in the sliding window to a great extent, the response error of the iteration of the k step still cannot be processed, if the response error is too large, the reliability of the approximate gradient after the median filtering is also seriously influenced, and at the moment, the introduction of exponential weighting is considered, namely, historical components are doped in the current approximate gradient, so that the response error of the iteration of the k step is eliminated.
Optionally, when the number of the Gamma modulation amounts is multiple, the weighted sum of the squares or absolute values of the relative errors between the minimized Gamma modulation amounts and the target Gamma modulation amount values may be used as the unconstrained optimization function by setting the weighting parameters corresponding to the multiple Gamma modulation amounts one to one.
And further, a first-order gradient approximation of an optimization problem objective function can be obtained by using the first-order gradient of the Gamma modulation quantity, wherein the first-order gradient of the Gamma modulation quantity can be a conservative optimal solution of the first-order gradient, or the first-order gradient is corrected by the Gamma modulation quantity for 1 time, or the first-order gradient is corrected by the Gamma modulation quantity for 2 times, so that the next modulation step can be obtained by using a step expression of a gradient descent algorithm.
And obtaining a second-order Hessian approximate value of the optimization problem target function by utilizing the first-order gradient approximate value of the optimization problem target function through a BFGS quasi-Newton method iteration format, and obtaining a next modulation step length according to a BFGS quasi-Newton method or a gradient descent algorithm step length formula by utilizing the first-order gradient approximate value and the second-order Hessian approximate value of the optimization problem target function. As an example, since the modulation step size of the gradient descent algorithm is more flexible than that of the quasi-newton algorithm, but is slower than that of the quasi-newton algorithm in convergence speed, and the modulation effects of the two modulation methods are respectively advantageous, the modulation method can be implemented by only using the quasi-newton algorithm or the gradient descent algorithm according to the requirements, or by alternately using the quasi-newton algorithm and the gradient descent algorithm, for example, setting the limit of the number of iterations, using the quasi-newton algorithm in a certain range of the number of iterations, switching to the gradient descent algorithm in another range of the number of iterations, and so on, until the modulation is finished.
Preferably, when the Gamma modulation amount is not converged and the reduction value of the unconstrained optimization function is smaller than a preset threshold value during each iteration, the next modulation step length is obtained by adopting a step length expression of a gradient reduction algorithm, otherwise, the next modulation step length is obtained by adopting the step length expression of a BFGS quasi-Newton method;
the method comprises the steps of initializing a register value of a panel to be modulated, a first-order gradient of a Gamma modulation quantity and a second-order Hessian matrix to obtain an initial value of a modulation step, wherein the initial value of the first-order gradient is obtained by random giving or fitting through data acquisition or pre-adjusting, and the initial value of the second-order Hessian matrix is obtained by approximate calculation of a linear part of the Gamma modulation quantity in a Hessian expression.
In the invention, the modulation difference is the difference between the Gamma modulation quantity of the current iteration and the Gamma modulation quantity of the appointed certain iteration, and the modulation step is the difference between the register value of the current iteration and the register value of the appointed certain iteration. And the modulation step length during the first iteration is a step length expression of a quasi-Newton algorithm, and the initial value of the second-order optimization parameter of the quasi-Newton algorithm is obtained through a second-order Hessian matrix.
In the invention, when the iteration is performed for the first time, the initial register value and the Gamma modulation amount first-order gradient need to be given, and the initial register value and the Gamma modulation amount first-order gradient can be randomly given without loss of generality.
In the invention, during the first iteration, the first-order gradient approximation of the objective function of the optimization problem is obtained by calculating the initialized first-order gradient of the Gamma modulation quantity, and the second-order Hessian approximation of the objective function of the optimization problem is obtained by calculating the initialized first-order gradient of the Gamma modulation quantity as a linear part. At this time, in order to achieve the purpose of speed increase, a first-order gradient of the Gamma modulation amount can be obtained through data acquisition and fitting or through pre-adjustment, a second-order Hessian initial value is calculated, the purpose of super-linear convergence can be achieved at the initial stage of the algorithm by combining a quasi-newton method, and as an example, after the Gamma modulation value is initialized randomly, Gamma adjustment of the first block of modules is started. Through the dynamic updating strategy, the first-order and second-order optimization information is gradually adjusted to be close to a reasonable value in the algorithm, when the second module is adjusted, the reasonable value is used for reinitializing the first-order gradient of the Gamma modulation quantity, and the initial convergence speed of the algorithm is improved to a great extent. In the subsequent module adjustment, the dynamic update value of the previous panel is continuously adopted to initialize the Gamma modulation amount first-order gradient, the convergence speed of the algorithm is still continuously improved and finally tends to be stable, and the final performance behavior of the algorithm does not depend on the initialization operation, so the algorithm can achieve the self-adaptive effect.
A computer-readable medium, in which a computer program executable by a terminal device is stored, causes the terminal device to perform the steps of the above-mentioned method when the program is run on the terminal device.
A terminal device comprising at least one processing unit and at least one memory unit, wherein the memory unit stores a computer program which, when executed by the processing unit, causes the processing unit to carry out the steps of the above-mentioned method.
Fig. 1 is a schematic diagram of an embodiment of the present invention. As shown in fig. 1, the method comprises the following steps:
step 1: initializing the register value of the panel to be modulated.
Step 2: and measuring the Gamma modulation quantity of the panel to be modulated.
And step 3: and judging whether the Gamma modulation quantity is converged. If yes, ending the single binding point adjustment; and if not, entering an iteration flow and turning to the step 4.
And 4, step 4: and judging whether the current iteration is the 0 th iteration, namely the first iteration. If yes, entering an initialization operation flow and turning to the step 5; otherwise, entering a dynamic updating process and turning to the step 8.
And 5: initializing a first-order gradient of the Gamma modulation quantity, and directly constructing an optimization problem objective function first-order gradient approximation and a second-order Hessian approximation by using the first-order gradient.
Step 6: and calculating the modulation step length according to a BFGS quasi-Newton method or a step length formula of a gradient descent algorithm by utilizing a first-order gradient approximation value and a second-order Hessian approximation value of an objective function of the optimization problem.
And 7: and (4) adjusting the register value according to the modulation step length, and turning to the step 2.
And 8: and establishing a sliding window with the capacity of n +1, and storing the current iteration data and the latest n times of iteration data.
And step 9: and establishing a sliding window with the capacity of n, and storing a first-order gradient conservative optimal solution of the Gamma modulation quantity. Each conservative optimal solution is a conservative optimal solution of the current iteration data and the iteration data in the last n times.
Step 10: and carrying out vector median filtering on the conservative optimal solution in the sliding window to obtain 1-time modified first-order gradient of the Gamma modulation quantity.
Step 11: and (4) carrying out convex combination on the first-order gradient of the Gamma modulation quantity for 1 time and the first-order gradient of the Gamma modulation quantity for the last iteration according to a preset weight to obtain the first-order gradient of the Gamma modulation quantity for 2 times.
Step 12: and (5) taking the Gamma modulation quantity first-order gradient corrected for 2 times as the final Gamma modulation quantity first-order gradient in the dynamic updating process, and constructing an optimization problem objective function first-order gradient approximation.
Step 13: and obtaining a second-order Hessian approximate value of the objective function of the optimization problem by using the first-order gradient approximate value of the objective function of the optimization problem through a BFGS quasi-Newton method iteration format. And (6) turning to the step.
Respectively selecting three different types of simulation responses, randomly initializing the first-order gradient of the Gamma modulation quantity for 10 times (equivalent to 10 different first initialization operations of an algorithm), initializing the first-order gradient of the Gamma modulation quantity again by adopting an internal dynamic update value after each adjustment is finished, carrying out the next adjustment, totally adjusting for 31 times (equivalent to continuously adjusting for 31 times on the same type of panel), including initializing for 30 times again, fixedly selecting initial values for each adjustment (200,200 and 50), and enabling the optimal solutions to be located at (100,100,100). The final convergence steps of the simulation responses of 3 different types are stabilized around 17 steps, 21 steps and 27 steps under the high-noise condition; under the condition of low noise, the final convergence steps of the 3 different types of analog responses are respectively stabilized near 4 steps, 5 steps and 6 steps, so that the final performance behavior of the method does not depend on the initialization operation, and the self-adaptive effect can be achieved.
Tables 1 and 2 are schematic diagrams comparing the adaptive combination method with the simplified newton method for high noise and low noise, respectively. According to the statistical results of 10000 times of comparison tests of the adaptive combination method and the simplified Newton method in the table, the simplified Newton method is only suitable for processing the condition of simple response (a first type) under the approximately approximate initialization state, and the adaptive combination method has obvious advantages in the aspects of convergence and convergence speed compared with the simplified Newton method when facing complex response and high noise environment.
TABLE 1 comparative schematic of adaptive combination method at high noise level and simplified Newton method
TABLE 2 comparison of adaptive combination method at low noise level with simplified Newton method
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A Gamma modulation method, comprising:
updating the register value of the panel to be modulated by using the modulation step length, and repeating the steps until the Gamma modulation quantity of the panel to be modulated meets the preset Gamma adjustment condition, wherein the current modulation step length is the difference value between the current register value and the last register value;
the obtaining process of the modulation step length is as follows:
constructing an unconstrained optimization function about the Gamma modulation quantity, wherein the unconstrained optimization function is the square or absolute value of the relative error between the Gamma modulation quantity and a Gamma modulation quantity target value;
obtaining a conservative optimal solution of a Gamma modulation quantity following conservative change on a first-order gradient of a register value, wherein the conservative change is an L2 norm of a difference value of the first-order gradient of the Gamma modulation quantity and a first-order gradient of a specified iteration Gamma modulation quantity; and acquiring the next modulation step by using the conservative optimal solution.
2. The Gamma modulation method according to claim 1, wherein the conservative optimal solution further satisfies a preset difference relationship, and the preset difference relationship is:
and constructing a trapezoid difference grid or Euler backward difference format by using the first-order gradient and a specified iteration first-order gradient, a modulation difference value and a modulation step length.
3. A Gamma modulation method according to claim 1, wherein obtaining the next modulation step using the conservative optimal solution comprises: correcting the conservative optimal solution according to a preset algorithm, combining the unconstrained optimization function to obtain a Gamma modulation step length,
wherein the predetermined algorithm comprises: and presetting a sliding window with the size of n, storing the conservative optimal solution inside, wherein each conservative optimal solution is the conservative optimal solution of the current iteration data and some iteration data in the latest n times, and performing vector median filtering on the conservative optimal solution in the sliding window to obtain 1-time modified first-order gradient of the Gamma modulation quantity.
4. A Gamma modulation method according to claim 3, wherein the predetermined algorithm further comprises:
and combining the Gamma modulation quantity first-order gradient corrected for 1 time with the last iteration Gamma modulation quantity first-order gradient according to a preset weight convex to obtain the Gamma modulation quantity first-order gradient corrected for 2 times.
5. A Gamma modulation method according to claim 1, wherein the initial value of the first order gradient of the Gamma modulation amount is obtained by random assignment or fitting through the collected data or by pre-adjustment, and the initial value of the register value is obtained by random assignment or fitting through the collected data.
6. The Gamma modulation method according to any one of claims 1 to 5, wherein when the number of the Gamma modulation amounts is plural, a weighting parameter corresponding to the plural Gamma modulation amounts is set, and a weighted sum minimizing a square or an absolute value of a relative error between the Gamma modulation amount and a target value of the Gamma modulation amount is used as the unconstrained optimization function.
7. The Gamma modulation method according to claim 6, wherein obtaining the next modulation step using the conservative optimal solution comprises:
and obtaining a first-order gradient approximate value of the optimization problem objective function relative to the register value by utilizing the first-order gradient of the Gamma modulation quantity, and obtaining the next modulation step by utilizing a step expression of a gradient descent algorithm.
8. The Gamma modulation method according to claim 7, wherein a second-order Hessian approximation of the optimization problem objective function is obtained by using the first-order gradient approximation of the optimization problem objective function through a BFGS quasi-Newton method iteration format, and a next modulation step is obtained by using the first-order gradient approximation and the second-order Hessian approximation of the optimization problem objective function according to a BFGS quasi-Newton method or a gradient descent algorithm step formula.
9. The Gamma modulation method according to claim 8, wherein, when the Gamma modulation amount is not converged and the unconstrained optimization function reduction value is smaller than a preset threshold value at each iteration, a step-size expression of a gradient descent algorithm is used to obtain a next modulation step-size, otherwise, a step-size expression of a BFGS quasi-newton method is used to obtain a next modulation step-size;
the method comprises the steps of initializing a register value of a panel to be modulated, a first-order gradient of a Gamma modulation quantity and a second-order Hessian matrix to obtain an initial value of a modulation step length, wherein the initial value of the first-order gradient is obtained by random giving or fitting through data acquisition or pre-adjusting, and the initial value of the second-order Hessian matrix is obtained by approximate calculation of a linear part of the Gamma modulation quantity in a Hessian expression.
10. A computer-readable medium, in which a computer program is stored which is executable by a terminal device, and which, when run on the terminal device, causes the terminal device to carry out the steps of the method of any one of claims 1 to 9.
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