WO2007147363A1 - Procédé et appareil de correction de la caractéristique gamma en communication vidéo - Google Patents

Procédé et appareil de correction de la caractéristique gamma en communication vidéo Download PDF

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WO2007147363A1
WO2007147363A1 PCT/CN2007/070121 CN2007070121W WO2007147363A1 WO 2007147363 A1 WO2007147363 A1 WO 2007147363A1 CN 2007070121 W CN2007070121 W CN 2007070121W WO 2007147363 A1 WO2007147363 A1 WO 2007147363A1
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gamma
function
luminance signal
luminance
gamma characteristic
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PCT/CN2007/070121
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English (en)
Chinese (zh)
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Zhong Luo
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Huawei Technologies Co., Ltd.
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Publication of WO2007147363A1 publication Critical patent/WO2007147363A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/14—Picture signal circuitry for video frequency region
    • H04N5/20—Circuitry for controlling amplitude response
    • H04N5/202—Gamma control

Definitions

  • the present invention relates to the field of video communication technologies, and in particular, to a method and apparatus for correcting gamma characteristics of video communication. Background of the invention
  • the process of video communication is: optical signals that need to be transmitted, such as people, backgrounds, files, etc., enter the video communication terminal (hereinafter referred to as the terminal) such as a camera/camera, etc., are converted into digital image signals by A/D, and then pressed. The code is reduced, transmitted, and reaches the other terminal. Then, it is decompressed and decoded to be restored to a digital image signal, and then displayed on the display device, and finally becomes an optical signal that is perceived by the human eye.
  • the image brightness signal has gone through multiple steps.
  • the Gamma property is a link where the input-output relationship of the image luminance signal is not linear, but a nonlinear relationship.
  • the image luminance signal (Luminance) here is a generalized luminance signal, that is, the initial optical signal, the electrical signal, and then the digitized image luminance/gray signal, and the signal of each phase contains the information of the image luminance signal. Therefore, in a broad sense, the image brightness signal passes through multiple links.
  • the numbers indicated in each of the blocks in Fig. 2 are luminance values, and the gray scale of the squares indicates the brightness of the luminance signal.
  • the brightness of the upper row of gray squares is linearly increasing, that is, increasing from 0.1 to 1.0, and the brightness of the next row of gray squares is incremented according to the power function, that is, the next line of gray
  • the brightness of the square is affected by the distortion of the Gamma nonlinearity.
  • the Gamma characteristic shown by the curve is given in Figure 2 (b).
  • the total Gamma property is equal to the composition of the Gamma function of each link.
  • G CT ⁇ .) G (1) (.) oG (2) (.) oG (3) (.) . ⁇ .. ⁇ Q oG ⁇ Q (j)
  • CT indicates cascaded total, which means the total gamma of the cascade.
  • Gamma nonlinearity is caused by different causes.
  • the Gamma characteristics of display devices such as CRT monitors are ideally:
  • the Gamma problem originated from the CRT display because its Gamma value is 2.2. To compensate for this nonlinearity, a Gamma value of 0.45 was introduced into the camera.
  • the form of the Gamma property is a Power Function. It should be noted that the input and output luminance signals here are normalized in their respective coordinate spaces, that is, 0 ⁇ L. ut ⁇ l, 0 ⁇ L in ⁇ l. While other types of displays such as liquid crystals, the form of the Gamma function may be different, or although it is also a power function in form, the parameters are different.
  • Gg(.) and Gc(.) are inverse functions of each other.
  • the inverse function is not necessarily solved, and even if there is an inverse function, it cannot be obtained by calculation.
  • the above-described Gamma correction method is implemented with the premise that the Gamma characteristic parameter can be determined for a given Gamma link or a cascade of a given Gamma link.
  • the Gamma characteristic parameter here is the parameter of the Gamma characteristic function curve.
  • the correction needs to involve more than two communication terminals.
  • the video of terminal A is transmitted to terminal B, and the correction of the video involves both the Gamma link on terminal A and the Gamma link on terminal B.
  • the Gamma characteristic parameters need to be determined during the gamma correction process.
  • Method 1 Instrument measurement method. The input luminance signal and the output luminance signal are measured, that is, some points on the Gamma characteristic function curve are measured by a special instrument, and then the data fitting method is used to perform curve fitting to determine the Gamma characteristic parameter.
  • the current implementation method for determining the Gamma characteristic parameter has a precondition, that is, the specific value of the input luminance signal and the output luminance signal of the Gamma link that needs to be determined by the Gamma characteristic parameter is clearly known. It is to know all the knowledge of the input luminance signal and the output luminance signal of the Gamma link, that is, the input luminance signal and the output luminance signal can be obtained. Therefore, the above two methods for determining the Gamma characteristic parameters are non-blind measurement methods.
  • the Gamma characteristic parameter measurement system uses the knowledge of the input luminance signal of the Gamma link and the full knowledge of the output luminance signal to measure the Gamma characteristic parameter.
  • the Gamma link can be a single Gamma link or a cascaded Gamma link.
  • Application scenario 1 For streaming media services and applications such as IPTV (Internet Protocol Television), since the program production process has been affected by the Gamma characteristics of the video input device, when the program is broadcast, especially on-demand, etc., It is not possible to obtain the Gamma characteristic of the video input device used to acquire the video signal during the original program production, and it is also impossible to measure the Gamma characteristic parameter of the video input device.
  • IPTV Internet Protocol Television
  • Application Scenario 2 The above problems also exist for applications such as data conferencing.
  • applications such as data conferencing.
  • the development of video conferencing and the development of data conferencing are synchronized, and the perfect combination of the two has great significance for collaborative applications.
  • the above-mentioned collaborative application business has strong market demand.
  • data conferencing applications the source of many multimedia materials such as pictures is untestable, it is difficult to obtain the Gamma characteristics of the video input device that generated the data at the time, and it is also impossible to perform the Gamma characteristic parameters of the video input device. Measured. '
  • the current non-blind measurement method for determining the Gamma characteristic parameter makes the Gamma correction difficult to apply, and the method of special instrument measurement improves the implementation cost of the video communication service.
  • Embodiments of the present invention provide a method and apparatus for correcting gamma characteristics of video communication, which reduces the cost of video communication and improves the ease of use of Gamma correction.
  • Embodiments of the present invention provide a method for correcting gamma characteristics of a video communication, including:
  • the mathematical relationship between the luminance density probability density function of the input and output luminance signals and the Gamma characteristic function is converted into: at the extreme point, the luminance signal is output a mathematical relationship between a luminance distribution probability density function and a Gamma characteristic function;
  • Gamma correction is performed on the gamma link according to the gamma characteristic parameter.
  • Obtaining a histogram module a luminance histogram for obtaining an output luminance signal, and outputting to the first conversion module;
  • a first conversion module configured to convert a luminance histogram of the received output luminance signal into an output luminance signal luminance distribution probability density function, and output to the extreme point calculation module and the second conversion module;
  • An extreme point calculation module configured to calculate respective extreme points of the brightness distribution probability density function of the received output luminance signal, and output to the second conversion module;
  • the storage module a mathematical relationship between an extreme point of the probability density function of the luminance distribution of the output luminance signal and an extreme point of the luminance density distribution probability density function of the input luminance signal, and a probability density of the luminance distribution of each of the input and output luminance signals
  • the mathematical relationship between the function and the gamma property function
  • the second conversion module is configured to: according to the extreme point received, the luminance signal distribution probability density function of the output luminance signal, and the mathematical relationship of the extreme points stored in the storage module, the respective brightness of the input and output luminance signals stored in the storage module
  • the mathematical relationship between the distributed probability density function and the gamma characteristic function is converted to: at the extreme point, the mathematical relationship between the luminance density distribution probability density function of the luminance signal and the Gamma characteristic function is output;
  • the Gamma characteristic parameter solving module is configured to solve and calculate the converted mathematical relationship to determine a gamma characteristic parameter; and the gamma correction module is configured to perform gamma correction on the gamma link according to the gamma characteristic parameter.
  • the gamma correction method provided by the embodiment of the present invention only needs to output a histogram of the luminance signal, and thus the gamma parameter determination method of the embodiment of the present invention may be referred to as a full blind measurement method.
  • the method does not require any knowledge of the input of the luminance signal. Therefore, the gamma correction method of the embodiment of the present invention has high application feasibility; thereby improving the gamma correction ease of use and broadening the gamma correction by the technical solution provided by the embodiment of the present invention.
  • the scope of application has improved the user experience and service quality.
  • Figure 1 is a schematic diagram of a model of the link Gamma characteristics
  • Figure 2 (a) is a schematic diagram of the Gamma characteristic
  • Figure 2 (b) is a schematic diagram 2 of the Gamma characteristic
  • Figure 3 is a schematic diagram of a model of gamma cascading with multiple links:
  • Figure 4 is a schematic diagram of the gamma correction principle for a Gamma link;
  • Figure 5 is a schematic diagram of the Gamma correction principle for a plurality of Gamma links
  • FIG. 6 is a schematic diagram of an implementation principle of a non-blind measurement method in the prior art
  • FIG. 7 is a schematic diagram showing an implementation principle of a method for determining a full-blind Gamma characteristic parameter according to an embodiment of the present invention.
  • Figure 8 is a diagram showing an example of a luminance histogram of a video signal
  • FIG. 9 is a schematic diagram showing a relationship between an input luminance signal and an output luminance signal luminance distribution probability density function according to an embodiment of the present invention.
  • FIG. 10 (a) is a frame input image in the prior art;
  • Figure 10 (b) is a frame output image in the prior art
  • Figure 10 (c) is a schematic diagram showing the correspondence between the extreme values of the luminance density distribution probability density function of the input luminance signal and the output luminance signal in the prior art;
  • FIG. 11 is a schematic diagram showing a probability density function of luminance distribution of an output luminance signal obtained by interpolation and data fitting according to an embodiment of the present invention
  • FIG. 12 is a schematic diagram showing the principle of a brute force search method according to an embodiment of the present invention.
  • Figure 13 is a schematic diagram of the initial hypercube and its peripheral multilayer hypercube in a two-dimensional case according to an embodiment of the present invention
  • Figure 14 is a schematic diagram of a brute force search method according to an embodiment of the present invention. Mode for carrying out the invention
  • Embodiments of the present invention may utilize only knowledge of the output luminance signal to determine Gamma characteristic parameters and perform Gamma correction. Since any knowledge of the input luminance signal is not utilized in the technical solution of the embodiment of the present invention, the method for determining the Gamma characteristic parameter in the technical solution of the embodiment of the present invention may be referred to as a full blind Gamma characteristic parameter determining method.
  • the embodiment of the present invention determines the Gamma characteristic parameter of the link based on the knowledge of the known output luminance signal.
  • all knowledge of the output luminance signal is known, but the embodiment of the present invention does not necessarily utilize the entire knowledge of the output luminance signal.
  • the entire knowledge of the output luminance signal is known as the output luminance signal.
  • the Gamma loop can be Gamma corrected according to the Gamma characteristic parameters.
  • the link that needs to determine the Gamma characteristic can be a single given Gamma link, or a cascade combination of multiple given Gamma links.
  • the embodiment of the present invention needs to obtain a luminance histogram of the output luminance signal, and the luminance histogram is as shown in FIG. 8.
  • the brightness of the set image is 0 to 255 levels, and the different brightness levels correspond to a brightness distribution probability.
  • a histogram is a technical term in the field of image processing technology.
  • a histogram can be a discrete form of distributed probability density function.
  • the video signal is composed of a continuous image of one frame and one frame, and the histogram of the output luminance signal can be obtained from a certain frame image.
  • the method of obtaining a histogram of a luminance signal from an image is a conventional technique and will not be described in detail herein.
  • the histogram of the output luminance signal can also be performed in other stages, such as obtaining a histogram of the output luminance signal when the output luminance signal is also a one-dimensional signal, and at this time, the output luminance signal is not converted into an image.
  • the luminance histogram can be directly obtained from the continuous distribution probability density function; conversely, the continuous distribution probability density function can also be obtained from the luminance histogram by means of data interpolation or fitting.
  • the histogram information can be obtained from the continuous distribution probability density function.
  • the overall set of luminance signals is
  • the entire set of luminance signals is a set of non-negative time signals whose overall signal amplitude is less than or equal to one.
  • the luminance signal here is a luminance signal in a general sense, so the following description of the relationship between the histogram information and the continuous distribution probability density function is applicable to both the input luminance signal and the output luminance signal. For the sake of simplicity of description, the relationship between the histogram information and the continuous distribution probability density function will be described below by taking the output luminance signal as an example.
  • these output luminance signals can be viewed as a random process.
  • the statistical characteristics of these output luminance signals may vary, but the output signals can be classified according to the statistical characteristics of the signals, especially according to the distribution probability characteristics.
  • Any signal as a stochastic process has a distribution probability density function corresponding to it. If the stochastic process is stationary (where the stationary is strictly in the sense of stability), then the distribution probability density function is independent of time; if the stochastic process is not stationary Then, this distribution probability density function may be related to time. Therefore, in general, for a stochastic process s(t) (tER, 0 ⁇ s(t) ⁇ 1 ), f s (x, t), te R can be used to represent its distribution probability density function.
  • n in equation (4) means English normalized, meaning normalization.
  • the distribution probability density function has the following properties:
  • the [0, 1] interval can be equally divided into N subintervals, each of which has a length of 1/N.
  • the normalized signal is restored to the unregulated signal space.
  • the luminance signal usually takes an integer of 0-255, and a total of 256 levels of brightness.
  • the luminance signal can also be generalized to 2 D -level brightness. In this case, it is necessary to linearly map the unit interval [0, 1] into a set ⁇ 0, 1, 2, 3, 2° -2, 2° -1 ⁇ , and each sub-interval is expanded by 2 D times. (1/N) 2 D . Then the corresponding probability sequence becomes a continuous probability density function -
  • the histogram can be directly obtained from the continuous distribution probability density function of the luminance signal.
  • the continuous distribution probability density function of the luminance signal can also be processed by data interpolation, fitting, etc. of the histogram. After getting it.
  • the gamma characteristic function may be selected from one of two gamma characteristic functions provided by the following embodiments.
  • the gamma characteristic function can also be other forms of function, as long as the gamma characteristic function satisfies continuous smoothness and at least second order is achievable.
  • the Gamma property function [ ⁇ , ⁇ , ..., is a parameter vector.
  • the parameter vector consists of ⁇ parameters. All or part of these parameters need to be determined. Therefore, according to this very general form, the Gamma property function only needs to satisfy the condition that the function is continuous, and, in general, the Gamma property function is smooth and steerable, at least segmentally smooth and steerable, therefore, assuming Gamma It is reasonable for the characteristic function to exist for the first and second derivatives of the variable X.
  • the first derivative of the Gamma property function can be represented by the following symbol: dx ( 15)
  • equation (20) is independent of time variable t.
  • the Gamma property function itself is independent of the time variable. Therefore, a set of gamma characteristic parameters are measured over a period of time, and the gamma characteristic parameters can be used throughout the communication during the period of time, such as in IPTV, a program.
  • the Gamma characteristic parameters can be considered to be the same, so that the Gamma characteristic parameters can be measured at the beginning of each program, and the Gamma characteristic parameters can be used throughout the implementation of the program.
  • (a) is a frame of the input video signal
  • (b) is a frame of the output video signal corresponding to (a)
  • the two curves in (c) are the brightness of the input luminance signal.
  • Distribution probability density function and output luminance signal luminance distribution probability density function are the two curves in (c) as the brightness of the input luminance signal.
  • the input luminance signal brightness distribution probability density function and the output luminance signal luminance distribution probability density function have J extreme points, respectively e, e 2 , ...., and n, r 2 Rj.
  • the extreme point of the luminance density distribution probability density function of the input luminance signal and the extreme value point of the luminance density distribution probability density function of the output luminance signal have the following two relationships: relationship 1, qualitative geometric topological relationship, ie two There is a one-to-one correspondence between the extreme points of the luminance density probability density function, that is, the number of extreme points of the two luminance distribution probability density functions is the same. Relationship 2, quantitative mathematical relationship: rk gp ⁇ p - Q ⁇ ; ⁇ ; ⁇ ...,; ⁇ ] . Of course, the mathematical relationship here allows A little change.
  • ⁇ ⁇ dc ⁇ - 1 + (d- l)c d , r d - 2 +(d- 2)c d . 2 r d - 3 +whi + c, .
  • the Gamma property function is monotonic, so the Gamma property function has an inverse function.
  • This inverse function can be written as g_ '( ⁇ ; ⁇ ).
  • the inverse of the Gamma property function is also dependent on the gamma parameter vector p.
  • Equation (26) does not contain any information about the input signal. Equation (26) is completely dependent on the output luminance signal brightness distribution probability density function and the output luminance signal luminance distribution probability density function. Value point. Thus, embodiments of the present invention are capable of determining Gamma characteristic parameters based solely on the output signal and its luminance distribution probability density function.
  • the histogram of the output luminance signal obtained by setting the embodiment of the present invention is: .., Nl ⁇ . That is, each histogram contains N items, and in the histogram terminology, each item is called a "bin".
  • the luminance density distribution probability density function of the output luminance signal can be obtained from the output image luminance histogram, and thus:
  • c d , c d-1 , c d-2 , ... c c is d+1 polynomial coefficients, and r is the amplitude of the output luminance signal.
  • This expression is a polynomial including a polynomial spline function.
  • N 256, at which point N is already large enough.
  • interpolation or data fitting techniques There are various implementation methods of interpolation or data fitting techniques, and embodiments of the present invention do not limit the specific implementation of interpolation or data fitting.
  • condition J ⁇ M-1 can be satisfied.
  • J equations are obtained from equation (26), and M-1 equations are arbitrarily selected to form a simultaneous solution of equations.
  • this set of equations is non-linear and transcendental, such as the Gamma property function in the form of a power function. Therefore there is no closed-form solution.
  • a numerical solution method is required. The numerical solution of the system of equations is a conventional technique and will not be described in detail in this embodiment.
  • the problem of determining the parameter vector p is transformed into a mathematical problem of finding the global minimum point of the cost function J(p).
  • the parameter vector p can be determined in the following three ways.
  • Method (3) Brutal Force Search Method.
  • brute force search is to search for all possibilities.
  • the set of all possible values of the parameter is a finite set. Then, by searching each point in the set one by one, the point that minimizes J(p) can be found, and thus the global minimum is found. Point p true . However, this situation is rare. In most cases, the parameters take continuous values. Therefore, the set of all possible values of the parameters is an infinite set, and the exhaustive search cannot be performed.
  • the brute force search method can divide the parameter space into a plurality of small hypercubes, and then take a point in each hypercube as a sample point, such as the geometric center point of the hypercube. Etc. Finally, calculate the function value of the cost function at the sampling point of each hypercube, find the sampling point of the hypercube that minimizes the cost function, and use the sampling point as the global minimum point.
  • a prior knowledge of the parameters can be utilized to find a suitable starting search point, so that Large reductions in the number of searches required, thereby increasing the efficiency of the brute force search method.
  • the set of all possible values of the M-1 parameters constitutes a parameter space (PS), and the parameter space is a subset of the M-1 dimensional European space RM- 1 .
  • the implementation method of the brute force search includes the following steps: Step 1: Hypercube partitioning.
  • the PS is divided into a plurality of M-1 hypercubes, in Fig. 12, ABCDEFGH, 8 points form a hypercube. Since each parameter has a different range of values, each side of the hypercube has a different length.
  • the geometric center of the hypercube is ⁇ 3 ⁇ 4 coordinates -
  • the initial value is generally around 2.2 for the video input device.
  • the Gamma parameter may be positive or negative due to manufacturing technology and product quality. Deviated from 2.2, but in most cases, it is closer to 2.2. In this case, if the search is started with 2.2 as the initial search point, since 2.2 is closer to the true value, the number of attempts to find the real value is less.
  • the search After determining the hypercube in which the initial search point is located, the search starts from the hypercube. Calculating J ( Pint ) according to the formula (30), if J (Piêt t ) can make the formula (36 ), or the searched hypercube satisfies a predetermined condition, such as the number of searched hypercubes reaches a predetermined value, etc., then , the entire search process ends, to step 5. At this point, Ptrue
  • the advantage is the parameter vector of the finally obtained Gamma property function.
  • Jthre S h. Ld is a predefined threshold.
  • Step 4. Continue searching.
  • the continuation search in step four can be done hierarchically.
  • the hypercube surrounding the outside of the initial hypercube can be one or more layers.
  • the middle gray square is the initial hypercube
  • the cube adjacent to the edge of the initial hypercube is the first layer hypercube
  • the cube adjacent to the edge of the first layer hypercube is the second layer hypercube
  • the second The cheap cube on the side of the layer hypercube is the third layer of hypercube.
  • the hierarchical search method of the embodiment of the present invention is: successively searching each of the hypercubes in each layer except the initial hypercube, and in each layer of the search for the hypercube, each of the layers should be traversed in a predetermined order.
  • the predetermined order may be varied, and the embodiment of the present invention does not limit the form of the predetermined order as long as it can traverse the hypercube in one layer.
  • the search process will end regardless of whether or not the geometric center Q of the hypercube that satisfies the condition (37) is found. At this time, the Q in the smallest J (Q) searched should be taken as Ptrue. Go to step five.
  • L is a predetermined threshold value indicating the number of layers that need to be searched at most.
  • Step five the search process ends.
  • the above search method can also be applied to achieve the fastest and best search effect in the process from coarse to fine.
  • the first search is performed in accordance with the above steps 1 through 5.
  • the first search can be seen as a rough search, this In this way, you can set the side length of the hypercube in the parameter space to be larger, so that the number of hypercubes in the parameter space is small.
  • the predetermined condition that the hypercube searched in step 3 satisfies may be whether the division granularity is coarser than the predetermined division granularity.
  • First search If a hypercube that satisfies condition (36) or (37) is found, the search process ends.
  • the first search process can use a hierarchical search method.
  • the second finer search is performed in the corresponding hypercube.
  • the smallest J (Q) corresponding hypercube searched for the first time should be regarded as the new entire parameter space.
  • the length of each hypercube in the new parameter space becomes smaller, and the search process from the first step to the fifth step is repeated.
  • the second search process ends.
  • the second search process can employ a hierarchical search method.
  • the hypercube that satisfies the condition (36) or (37) is not found in the second finer search process, and the granularity of the hypercube has not reached the predetermined granularity, the smallest one found in the second search.
  • the third finer search is performed in the corresponding hypercube of J (Q), and so on, the geometric center of the hypercube that satisfies the condition (36) or (37) found by the last fine search is taken as the global best advantage Pttue, That is, the parameter vector of the Gamma property function.
  • the search process ends. At this point, the collection center of the hypercube corresponding to the smallest J (Q) found should be taken as the global best advantage Ptrue, the parameter vector of the SPGamma property function.
  • a hierarchical search method can be employed in each of the above-described search processes from coarse to fine.
  • the Gamma link can be corrected by Gamma.
  • the link that needs to perform the gamma correction may be a video data source device, an intermediate device in the video communication network, or a video data destination device.
  • the gamma correction method provided by the embodiment of the present invention only needs to know the histogram of the output luminance signal and a set of loose assumptions to determine the gamma characteristic parameter of a given gamma link, thereby
  • the multimedia communication system provides an easy-to-implement gamma correction method.
  • the gamma correction method provided by the embodiment of the present invention has high application feasibility, thereby greatly broadening the application range of gamma correction, especially for IPTV, collaborative data conference, Public video communication using low-end video input devices provides a good gamma correction function, greatly improving user experience and service quality, further enhancing the competitiveness of these services, and bringing huge benefits to telecom operators, service providers and equipment manufacturers. Economic benefits.
  • the apparatus for correcting video communication gamma characteristics mainly includes: acquiring a histogram module, a first conversion module, an extreme point calculation module, a storage module, a second conversion module, a Gamma characteristic parameter solving module, and a gamma correction Module.
  • the acquisition histogram module is mainly used to obtain a luminance histogram of the output luminance signal, and output the obtained luminance histogram to the first conversion module.
  • the acquisition histogram module can obtain a luminance histogram of the output luminance signal before converting the output luminance signal into an output image frame, and can also obtain a luminance histogram of the output luminance signal from the output image frame. Specifically, it is described in the above method.
  • the first conversion module is mainly configured to convert a luminance histogram of the received output luminance signal into a luminance density distribution probability density function of the output luminance signal, and output the luminance signal distribution probability density function of the output luminance signal to the extreme point calculation module and the second conversion respectively.
  • the extreme point calculation module is mainly configured to calculate each extreme point of the brightness distribution probability density function of the output brightness signal after receiving the brightness distribution probability density function of the output brightness signal, and transmit the calculated extreme points to the second conversion Module.
  • the method of calculating the extreme point of the luminance density distribution probability density function of the luminance signal is a conventional technique and will not be described in detail herein.
  • the storage module is mainly used for storing the mathematical relationship between the extreme point of the luminance density distribution probability density function of the output luminance signal and the extreme point of the luminance density distribution probability density function of the input luminance signal, and storing the luminance density probability density of each of the input and output luminance signals.
  • the mathematical relationship between the extreme point of the brightness distribution probability density function of the output luminance signal and the extreme point of the luminance density distribution probability density function of the input luminance signal may be: rk gie ppf; ⁇ / ⁇ , ⁇ ,...,/ ⁇ ] ⁇
  • the mathematical relationship here allows for a slight transformation, as described in the above method.
  • the second conversion module is mainly used for respectively distributing the brightness distribution of the input and output luminance signals in the storage module according to the extreme points received, the luminance density distribution probability density function of the output luminance signal, and the mathematical relationship of the extreme points stored in the storage module.
  • k l , 2, 3 , J
  • J is the number of extreme points of the luminance distribution probability density function
  • r k is the extreme point of the luminance distribution probability function of the output luminance signal
  • the derivative function ⁇ ⁇ is:
  • the Gamma characteristic parameter solving module is mainly used to solve the mathematical relationship after the conversion of the second conversion module to determine the gamma characteristic parameter.
  • the Gamma characteristic parameter solving module can determine the gamma characteristic parameters by directly solving the equation method, the nonlinear function optimization method, and the like.
  • the nonlinear function optimization methods herein include conventional mathematical optimization methods, neural network methods, brute force search, and the like.
  • the Gamma characteristic parameter solving module can adopt a hierarchical search method when using the brute force search method, or a hierarchical search method from coarse to fine, as described in the above method.
  • the gamma correction module is mainly used for gamma correction of the gamma link according to the Gamma characteristic parameter obtained by the Gamma characteristic parameter solving module.
  • the gamma link here includes: a cascading combination of a given Gamma link or multiple given Gamma links.
  • the device provided by the embodiment of the present invention is located in a video device, such as in a video data source device, in an intermediate device of a video communication network, and in a video data destination device.

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Abstract

L'invention concerne un procédé et un appareil de correction de la caractéristique gamma en communication vidéo, selon lequel on obtient l'histogramme de luminance du signal de sortie qui est converti en fonction de densité de probabilité de la distribution de la luminance du signal de sortie, on détermine les points extrêmes de ladite fonction; on établit le rapport mathématique entre les points extrêmes de la fonction de densité de probabilité de la distribution de la luminance du signal de sortie et les points extrêmes de la mêmefonction du signal d'entrée; on convertit le rapport mathématique entre les fonctions de densité de probabilité de la distribution de la luminance du signal de sortie et du signal d'entrée et la fonction de la caractéristique gamma en un rapport mathématique entre la fonction de densité du signal de sortie et la fonction de la caractéristique gamma sur les points extrêmes au moyen du rapport mathématique des points extrêmes; on résout le rapport mathématique converti pour déterminer le paramètre gamme et exécuter la correction gamma à l'étape gamma. Le procédé de détermination du paramètre totalement aveugle de la caractéristique gamma de ce mode de réalisation facilite la correction gamma et élargit le champ d'application de la correction gamma.
PCT/CN2007/070121 2006-06-15 2007-06-15 Procédé et appareil de correction de la caractéristique gamma en communication vidéo WO2007147363A1 (fr)

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