CN1889693A - Gamma characteristic correcting method and detecting method for determining equivalent model and parameter thereof - Google Patents

Abstract

The present invention relates to video communication technology, and particularly discloses the gamma characteristic equivalent model determination, equivalent model parameter detection method and correction method in the signal transmission and processing links, so as to solve the gamma equivalent model selection and model parameter measurement in the prior art Inaccurate, so that the gamma characteristics in the signal transmission and processing links cannot be accurately analyzed and corrected. The method includes the following steps: determining a correction point, N _p links after N _a links before the correction point; selecting the first equivalent model equivalent to the gamma characteristics of the N _a links and determining its first Inverse model, select the second equivalent model equivalent to the gamma characteristics of the N _p links and determine its second inverse model; construct a correction link model according to the first inverse model and the second inverse model, use the The correction link model determines the correction signal of the final output signal of the N _a links and inputs the correction signal to the N _p links.

Description

Correction method of gamma characteristic and detection method of determining equivalent model and its parameters

technical field

The invention relates to video communication technology, in particular to a detection method and a correction method for determining a gamma characteristic equivalent model and its parameters in video signal transmission and processing links.

Background technique

Gamma correction is a common problem in multimedia information systems that needs to be solved. For multimedia information systems, video/still images captured by cameras/cameras, computer-generated graphics/animations, and video/still images from other terminals in communication etc. In order to achieve a high-quality display effect on the display device and obtain a good user experience (User Experience, or Quality of Experience), Gamma correction must be performed.

The situation that the multimedia information system related to the present invention involves Gamma correction includes the following aspects:

Situation A: local terminal system, information is mainly processed on the local terminal and does not involve the communication process. Mainly the video/still image captured by the local camera/camera, computer-generated graphics/animation, etc. are displayed on the local display. Such as PC, PDA (Personal Digital Assistant) or mobile phone with camera function.

Case B: Multi-terminal/terminal-server communication system, communication between multiple terminals, or communication between a terminal and a server. Mainly videophones, high-end (2.5G/3G, B3G) mobile phones, PDAs, etc. with MMS (Multimedia Message Message) or video communication functions.

Video communication is currently being widely used with the rapid development of broadband networks. Domestically and internationally, video conferencing and videophone services are becoming basic services on NGN (Next Generation Network). Telecom operators in various countries also attach great importance to this market opportunity. It can be expected that in the next few years, video communication services will become an important business growth point for operators. A key issue in developing this type of business is to improve the end-to-end (End-to-end) user experience (UserExperience, or Quality of Experience). In user experience, in addition to network QoS (packet loss, delay, jitter, R factor, etc.) parameters, for video, the Gamma nonlinearity caused by each link causes distortion of the brightness signal, which also affects the end user experience. important factor. However, at present, the methods and technologies for improving end-to-end user experience are mainly focused on ensuring network QoS and video compression coding-related pre-processing (Post-processing), but for the brightness distortion problem caused by Gamma characteristics, there is a lack of However, the seriousness of this problem has attracted the attention of some major international telecom operators. France Telecom (France Telecom) recently proposed in the International Telecommunication Union ITU-T to consider the impact of Gamma characteristics on communication user experience in video communication, and to solve such problems.

In a multimedia information terminal (hereinafter referred to as the terminal), the optical signal of the external scene (person, background, file, etc.) enters the camera/camera, and is converted into a digital image signal by A/D, and then:

In case A, it is directly sent to the display device for display, and finally becomes a light signal to be perceived by human eyes. Of course, there may be some processing in the middle, such as image enhancement to improve the image effect;

In case B, the digital image signal is compressed by a compression encoder (Encoder), and then transmitted through the network (wired, wireless, circuit switching, packet switching network can be used) to reach the other terminal, and the other terminal passes through the decoder (Decoder). Compression (decompression) is decoded and restored to a digital image signal, which is then displayed on a display device, and finally becomes a light signal to be perceived by the human eye.

As shown in Figure 1, Figure 1 is a schematic diagram of the model of the Gamma characteristic of the link. No matter in case A or case B, the image luminance signal (Luminance, here is a generalized luminance signal, that is, the initial optical signal to electrical signal , and then to the digitized image brightness/grayscale signal, the signal at each stage contains the information of the brightness signal, so in a broad sense, the brightness signal has gone through multiple links) through multiple links. By definition, the Gamma characteristic means that the input-output relationship of a luminance signal in a link is not linear but nonlinear, as shown in the curve in Figure 10 .

The influence of Gamma nonlinear link distortion is shown in Figure 2. The brightness of the upper row of gray squares increases linearly, from 0.1 to 1.0. The lower row is distorted by the Gamma nonlinear link, and the brightness increases according to the law of the power function.

In practice, Gamma nonlinearity is caused by different reasons. For CRT (Cathod Ray Tube, cathode ray tube) display devices such as TVs and PC monitors, the Gamma characteristic satisfies Formula 1 under ideal conditions:

L _out =L _in ^2.2 (1)

Formula 1 is a power function (Power Function). It should be noted that the input and output luminance signals here are normalized (Normalized) in their respective coordinate spaces, that is, 0≤L _out ≤1, 0≤L _in ≤1.

For other types of displays such as mobile phones and PDAs, such as liquid crystals, etc., the form or form of the Gamma function is different, or the parameters are different.

As shown in Figure 3, Figure 3 is a schematic diagram of the model of the Gamma characteristics of multiple links cascaded (cascading or called in series), and the total Gamma characteristics are equal to the composition of the Gamma functions of each link, which satisfies Formula 2:

G _CT (.)＝G ⁽¹⁾ (.)оG ⁽²⁾ (.)оG ⁽³⁾ (.)……G ^(n-1) (.)оG ⁽ⁿ⁾ (.)

l _out ＝GCT(l _in )＝G(n)(G ^(n-1) (G ^(n-2) (...G ⁽²⁾ (G ⁽¹⁾ (l _in ))))) (2)

"о" indicates the compound operation of the function. CT stands for cascaded total, which means cascaded total Gamma.

For case A: the multiple Gamma links involved are shown in Figure 4, mainly including:

1. Camera/camera Gamma, expressed as G _Cam (.);

General cameras have Gamma characteristics. In addition to the nonlinearity of imaging devices such as CCD itself, the camera introduces artificial nonlinearity. The purpose is to make the Gamma characteristics of the camera just compensate for the Gamma characteristics of the display, so that the total Gamma characteristics are linear. . If the ideal Gamma of the display is: L _out = Lin2.2; then the ideal Gamma of the camera is: L _out = _Lin ^0.45 .

Therefore, in theory, the Gamma characteristics of the camera are determined by the Gamma characteristics of the display. However, due to the increasing complexity of the terminal system, there are multiple links between the camera and the display, the number of which is uncertain, and their respective Gamma characteristics are also unknown, so that even if the Gamma of the camera and the display match exactly, they can compensate each other, but because of the existence of intermediate links, this compensation Generally invalid. And there are many types of displays, such as: CRT and liquid crystal, plasma and other displays, their Gamma characteristics are quite different, and the Gamma characteristics of cheap cameras often seriously deviate from their ideal Gamma.

2. Store the file Gamma, expressed as G _Fil (.);

The file may come from a camera, has been processed, compressed and encoded, and has also gone through multiple gamma links, so the file itself already carries the gamma characteristic.

3. Display frame memory Gamma, expressed as G _FBuf (.);

Because the color depth of the display storage in the early display is not enough, for example, it can only support 4-bit, 8-bit, and 16-bit color depth, instead of the ideal 24-bit true color, which is equivalent to compressing the dynamic range of the input brightness signal, so Gamma is also introduced. characteristic. In addition, because in the non-true color mode, the used palette (Palette) color mapping technology or dithering (Dither) technology will introduce non-linear Gamma.

4. Display the look-up table Gamma, expressed as G _LUT (.);

Some display devices, in order to compensate for the non-linearity of the display, artificially introduce Gamma, which is represented as a LUT (Look-Up Table), and the brightness data read from the frame memory must be converted by the LUT to drive the display.

5. Display Gamma, expressed as G _Disp (.).

General monitors have strong Gamma non-linearity.

For case B: the multiple Gamma links involved are shown in Figure 5, mainly including:

1. Camera/camera Gamma, expressed as G _Cam (.);

2. Store the file Gamma, expressed as G _Fil (.);

3. Display frame memory Gamma, expressed as G _FBuf (.);

4. Display the look-up table Gamma, expressed as G _LUT (.);

5. Display Gamma, expressed as G _Disp (.);

6. Encoder Gamma, expressed as G _Enc (.);

Gamma caused by DCT (Discrete Cosine Transform) transformation and quantization in compression.

7. Decoder Gamma, expressed as G _Dec (.).

Gamma caused by DCT inverse transformation and inverse quantization in decompression.

For case B, what is more serious is that the Gamma links of local video/image, remote video/image and self-loop video/image (used for special purposes such as fault diagnosis, etc.) are different. In addition, no matter case A or B. There may be more Gamma links involved in the real situation, so the situation is more complicated.

The ideal situation is that there is a linear relationship between the input light signal and the output light signal from entering the camera to finally displaying the output light signal on the display, that is: L _out = L _in , so that the scene seen by people is the same as the original Exactly the same, best user experience.

To obtain a linear relationship, Gamma Correction (Gamma Correction) must be performed on links with nonlinear Gamma characteristics. As shown in Figure 6, for a link, its Gamma characteristic is given, then another correction link can be used to cascade with it, so that the total Gamma characteristic after cascading is called a true linear relationship, thus achieving For the purpose of compensating the nonlinearity of a given link, the model of the correction link is the inverse model of the equivalent model of Gamma characteristics. If the equivalent model can be expressed by a functional relational expression, then the functional relational expression of the inverse model is its inverse function. Obviously, G _g (.) and G _c (.) are inverse functions of each other. In general, for a function, there may not necessarily be a solution to obtain its inverse function (or even if the solution exists, it cannot be obtained by calculation).

In more practical applications, as shown in Figure 7, the correction link needs to be inserted between the two given links before and after. At this time, the situation of G _c (.) is more complicated, and G _c (.) and G _a (.) or G _p (.) is no longer a simple inverse functional relationship.

There are mainly two methods for realizing the correction link in the prior art:

Existing technology 1: Correct the Gamma characteristics of the display completely relying on the Gamma characteristics of the camera/camera or the display LUT

Assuming an ideal state: G _Cam (.): L _out = L _in ^0.45 ; G _LUT (.): L _out = L _in ^0.45 ; G _Disp (.): L _out = L _in ^2.2

Then: G _Cam оG _Disp (.) becomes: L _out =L _in , forming a standard linear relationship; G _LUT оG _Disp (.) becomes: L _out =L _in , forming a standard linear relationship.

But there is following deficiency in above-mentioned technology:

The ideal state is difficult to obtain, and there is no guarantee that the Gamma of the camera/camera and LUT will exactly match the Gamma of the monitor. And there are many types of displays, and the Gamma of a cheap camera is definitely not ideal; if G _Cam (.) and G _LUT (.) exist at the same time, the compensation is over-compensated, and G _Cam оG _LUT оG _Disp (.) becomes: L _out = L _in ^0.45 , it deviates from linearity; the mathematical model for simulating the gamma characteristics is inaccurate. Many studies have shown that the gamma of the display is not a simple power function, and a more accurate model may be a linear combination of a power function and a constant function, or a Composition of linear and power functions.

Prior art two:

Between some links, such as after the camera link or before the display frame storage link, a Gamma correction link is inserted to perform Gamma correction. In addition, a more accurate model may be used in terms of the Gamma characteristic model of the display, such as formula 3:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{c}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 0.45</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; if</span>}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 0.081</span>}\\ \frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 1.099</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.099</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2.2</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; if</span>}\mathrm{<span\; class="notranslate">\; 0.081</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

Correspondingly, the Gamma of the camera is considered to exactly match the Gamma of the display, such as formula 4:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; 0.45</span>}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; if</span>}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 0.081</span>}\\ \mathrm{<span\; class="notranslate">\; 1.099</span>}{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}}^{\mathrm{<span\; class="notranslate">\; 0.45</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.099</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; if</span>}\mathrm{<span\; class="notranslate">\; 0.081</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

Prior art two shortcoming is:

The correction model is single, but the situation of multiple links is very complicated. The Gamma characteristics of multiple links cannot be accurately obtained, which will inevitably lead to inaccurate correction, that is, the correction result is still nonlinear, and even if the model is more accurate, it will not help. Therefore, the problem of over-correction or under-correction cannot be avoided; and this method can only be applied in some specific situations, and cannot be used for the correction of any number of Gamma links.

Contents of the invention

The invention provides a detection method for determining the gamma equivalent model and its parameters to solve the inaccurate gamma equivalent model selection and model parameter measurement in the prior art, so that it is impossible to accurately analyze and correct the signal transmission and processing links. Gamma characteristics;

At the same time, based on the above detection method, a correction method applicable to the gamma characteristics of both single-link and multi-link is provided, so as to solve the problem that the existing correction methods cannot be used universally.

A method for correcting gamma characteristics, used to correct N _t links with gamma characteristics included in the multimedia information system or a specific signal during transmission and processing of the multimedia information system, the method comprising the following steps:

determining a correction point, which divides the N _t links into N _a links before the correction point and N _p links after the correction point, wherein: N _a ≥ 0, N _p ≥ 0, N _a + N _p = N _t ;

determining the first equivalent model and its first inverse model equivalent to the gamma characteristics of the N _a links, and determining the second equivalent model and its second inverse model equivalent to the gamma characteristics of the N _p links;

Constructing a correction link model according to the first inverse model and the second inverse model, using the correction link model to determine the correction signal of the final output signal of the N _a links and inputting the correction signal into the N _p links.

When the equivalent model adopts a functional representation, the representation function of the inverse model is the corresponding inverse function.

The determining the first equivalent model or the second equivalent model includes the following steps:

A1. Detect respectively N values L ^P _out (i) of the actual output signal generated by inputting N sampling values L _in (i) of the input signal into the link, wherein: 0≤i≤N-1;

A2. Select a model to be tested from a group of alternative equivalent models using functional representations to perform step A3;

A3. For the model to be tested, select a set of initial parameters;

A4. Calculation:

Calculating N values L ^M _out (i) of theoretical output signals corresponding to the N sampling values according to the model to be tested, wherein: 0≤i≤N-1; and

an objective function value F, which is related to the difference between each pair of corresponding L ^P _out (i) and L ^M _out (i);

A5. Judging whether the objective function value F is equal to or less than the set threshold value, if so, it is considered that the model to be tested is accepted as the final equivalent model, and the parameter corresponding to the minimum objective function value F After being used as the parameter of the equivalent model, turn to step A8; otherwise, perform step A6;

A6. Determine whether the number of executions of step A4 reaches the limited number of iterations, if so, select one from other undetected alternative equivalent models as the model to be tested and return to step A3; otherwise, execute step A7;

A7, using the mathematical optimization method to adjust the model parameters, return to step A4;

A8. End.

In the step A5, when the objective function value F is equal to or less than the set threshold value, then according to the set number of cycles, use the mathematical optimization method to adjust the parameters and calculate the objective function value F , and then take the parameter corresponding to the minimum objective function value F as the parameter of the equivalent model.

Described method also comprises the steps:

respectively bringing the measured parameters into the representation function of the corresponding inverse model, and obtaining a first inverse function corresponding to the first inverse model and a second inverse function corresponding to the second inverse model;

A calibration model is constructed using the first inverse function formula and the second inverse function formula.

The construction method of the correction link model includes one of the following:

Direct calculation method: use the composite function of the first inverse function and the second inverse function to calculate the correction signal of the final output signal of N _a links in real time;

Two-step calculation method: use the first inverse function to calculate the primary correction signal of the final output signal of N _a links in real time, use the second inverse function to calculate the secondary correction signal of the primary correction signal, and use the secondary correction signal as the correction signal;

Look-up table method: in advance, according to the direct calculation method or two-step calculation method, calculate the corresponding correction values of a plurality of sampling values in the value interval of the final output signal of the N _a links, and save the corresponding relationship In a data table, the correction value for any value to be corrected is then determined by querying the data table in real time.

In the table look-up method: when the input value to be corrected is in the data table, the corresponding correction value is directly obtained by looking up the table; when the input value to be corrected is not in the input column of the data table, linear interpolation average The method to obtain the corresponding correction value.

The objective function value F satisfies the following conditions:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}^{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}^{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

The functional relationship of said group of alternative equivalent models and their corresponding inverse functions include:

The gamma characteristic equivalent model function relational formula is: L _out =pL _in ^α +(1-p), wherein: the definition domain of this function is the interval [0,1], and the value range is the interval [(1-p ), 1]; then the inverse function relation of this function is:

$L_{\mathrm{out}}={(\frac{1}{p}{L}_{\mathrm{in}}+(1-\frac{1}{p}))}^{\frac{1}{\mathrm{\&alpha;}}};$ or,

The equivalent model function relational expression of described gamma characteristic is: $L_{\mathrm{out}}={({\mathrm{QUR}}_{\mathrm{in}}+(1-q))}^{\frac{1}{\mathrm{\&beta;}}},$ Among them: the definition domain of the function is the interval [1-1/q, 1], and the value range is the interval [0, 1]; then the inverse function relation of the function is:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; q</span>}}{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}}^{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; q</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them: L _in is the input signal value, L _out is the output signal value, p and α, q and β are the parameters to be measured respectively; and when N _a =1 or N _p =1: 0<p≤1, α ≥1, q≥1, β≥1; when N _a >1 or N _p >1: 0<p≤1, α≥0, q≥1, β≥0.

The mathematical optimization method includes but is not limited to one of the following methods: hill climbing method; 0.618 method; steepest descent method; conjugate gradient method.

The N sampling values of the input signal are selected in the interval [0, 1].

When the equivalent model is in the form of a data table, the corresponding inverse model is the inverse table of the data table.

A detection method for determining a gamma characteristic equivalent model and parameters thereof, the gamma characteristic equivalent model being used for equivalent signal transmission or gamma characteristics in a processing link, comprising the following steps:

B1. Respectively detect N values L ^P _out (i) of the actual output signal generated by inputting N sampling values L _in (i) of the input signal into the link, wherein: 0≤i≤N-1;

B2. Select a model to be tested from a group of alternative equivalent models in the form of a function to perform step B3;

B3. For the model to be tested, select a set of initial parameters;

B4. Calculation:

Calculate N values of the theoretical output signal corresponding to the N sampling values of the signal according to the model to be tested: L ^M _out (i), where: 0≤i≤N-1; and

an objective function value F, which is related to the difference between each pair of corresponding L ^P _out (i) and L ^M _out (i);

B5. Judging whether the objective function value F is equal to or less than the set threshold value, if so, it is considered that the model to be tested is accepted as the final equivalent model, and this group of parameters is used as the parameter of the equivalent model Then go to step B8; otherwise, go to step B6;

B6. Determine whether the number of executions of step B4 reaches the limited number of iterations, if so, select one from other undetected alternative equivalent models as the model to be tested and return to step B3; otherwise, execute step B7;

B7. Using the mathematical optimization method to adjust the parameters, return to step B4;

B8. End.

In the step B5, when the objective function value F is equal to or less than the set threshold value, then according to the set number of cycles, use the mathematical optimization method to adjust the parameters and calculate the objective function value F , and then take the parameter corresponding to the minimum objective function value F as the parameter of the equivalent model.

The beneficial effects of the present invention are as follows:

The invention solves the selection problem of the Gamma characteristic model and the detection problem of the parameters and the correction problem of the Gamma characteristic of the link which are common in the multimedia information system, and provides a general correction method for the comprehensive Gamma characteristic of any multi-link cascade. Therefore, the user experience of the multimedia information system can be greatly improved.

Description of drawings

Figure 1 is a general model of the link Gamma characteristic;

Fig. 2 is a schematic diagram of brightness signal distortion caused by link Gamma characteristics;

Figure 3 is a general model of multi-link cascaded Gamma characteristics;

FIG. 4 is a schematic diagram of multiple Gamma links in case A;

Fig. 5 is a schematic diagram of multiple Gamma links in case B;

Fig. 6 is a schematic diagram of the Gamma characteristic of a single link of correction;

Fig. 7 is a schematic diagram of the Gamma characteristic of correcting multiple given links;

Fig. 8 is a schematic diagram of determining correction points when applying the correction method of the present invention;

Fig. 9 is a schematic diagram of using two sub-links to perform front and rear correction according to the present invention;

Fig. 10 is a schematic diagram of the Gamma characteristic function curve;

Fig. 11a and Fig. 11b are the schematic diagrams of the Gamma characteristic model function curve respectively;

Fig. 12 is a schematic diagram of the limited area of the function curve of the Gamma characteristic model.

Detailed ways

Studies have shown that the functional relationship of a more accurate gamma-characteristic equivalent model is a linear combination of a power function and a constant function, or a compound of a linear function and a power function, in different environments, by actually measuring the parameters in the functional relationship The specific functional relationship can be obtained more accurately.

Embodiment 1: Measurement method of gamma characteristic equivalent model parameters

The invention selection takes the following single-link equivalent model as a general model of Gamma characteristics as an example

Detailed description:

The first type of Gamma model: L _out = pL _in ^α + (1-p) 0<p≤1, α≥1 (5)

Among them: the domain of definition of the function shown in formula 5 (that is, the value range of the independent variable) is the interval [0, 1], the value range (the value range of the function value) is the interval [(1-p), 1], and the curve characteristics As shown in Fig. 11a, obviously for the first type of model, if p=1, α=1, then L _out =L _in . Therefore, in actual measurement, if the parameters of the first type of model are p=1, α=1, or p is sufficiently close to 1 and α is sufficiently close to 1, it can be considered that the gamma characteristic of the link can be ignored without correction. A typical example of this first type of gamma characteristic is a CRT monitor.

The second type of Gamma model: $L_{\mathrm{out}}={({\mathrm{QUR}}_{\mathrm{in}}+(1-q))}^{\frac{1}{\mathrm{\&beta;}}}---q\&Greater\; Equal;1,\mathrm{\&beta;}\&Greater\; Equal;1---\left(6\right)$

Wherein: the domain of definition of the function shown in formula 6 (that is, the value range of the independent variable) is the interval [1-1/q, 1], and the value domain (the value range of the function value) is the interval [(0, 1], the curve The characteristics are shown in Figure 11b. Obviously, for the second type of model, if q=1, β=1, then there is L _out =L _in . Similarly, in actual measurement, if q=1, β=1, or q When β is sufficiently close to 1 and β is sufficiently close to 1, it can be considered that the gamma characteristic of the link can be ignored without correction, and a typical example of this second type of characteristic is a camera.

In addition, if q=1/p, α=β, then the first type and the second type of models are inverse functions of each other, so they can compensate each other to obtain linear characteristics. That is: if a given link object has the first-type Gamma characteristics, then its corrected Gamma characteristics have the second-type model; if a given link object has the second-type Gamma characteristics, then its corrected Gamma characteristics have the first-type model.

After multiple Gamma links are cascaded, their comprehensive Gamma characteristics, in terms of mathematical generality, no longer have the first or second type of single-link models. However, we found in the research that the mathematical model of multi-link integrated Gamma characteristics has the following characteristics:

1. Its function image is limited to the area [0, 1]×[0, 1] of the coordinate plane (where “×” represents the Cartesian Product of two sets, or called the direct product), as shown in Figure 12 ;

2. Monotonically increasing;

3. Convex up and down;

In the two cases, geometrically speaking, convex means that the curve bends upward to the left, and convex means that the curve bends downward to the right.

4. Intersect with the Lin axis, or intersect with the Lout axis (two cases);

5. The curve passes through the (1,1) point;

Therefore, the independent combination of situations 3 and 4 forms four compound situations:

Case 1, intersects with the Lin _axis and is convex;

Case 2, intersects with the Lin _axis and is convex downward;

Case 3, intersects with the L _out axis and is convex;

Case 4, intersects with the L _out axis and is convex downward.

According to the above qualitative analysis, the comprehensive Gamma characteristic model of cascading multiple Gamma links can have the following two types, and the functional relations are formula 7 and formula 8 respectively:

The first category: L _out ＝pL _in ^α +(1-p) 0<p≤1, α>0 corresponds to cases 3 and 4 (7)

The second category: $L_{\mathrm{out}}={({\mathrm{QUR}}_{\mathrm{in}}+(1-q))}^{\frac{1}{\mathrm{\&beta;}}}$ q≥1, β＞0 corresponds to cases 1 and 2 (8)

It should be pointed out that from a formal point of view, multi-links (wherein there can be first-type links and second-type links, and the two types of links can be cascaded according to any number and order, and the method of the present invention is applicable) the comprehensive Gamma characteristic The first type and the second type are the same as the first type and the second type of the single-link Gamma characteristic respectively. However, in the first type of comprehensive characteristic model, according to the qualitative analysis results and actual measured experience values, the value range of index α becomes α>0, while in the second type of comprehensive characteristic model, the value range of index β becomes β> 0.

After the Gamma characteristic model is selected, it is necessary to measure the Gamma characteristic parameters in the specific application environment. The measurement method is directly related to whether the functional relationship satisfied by the input and output signals in the final Gamma characteristic model is accurate. Among them: for the first type of model, parameters p and α need to be determined; for the second type of model, parameters q and β need to be determined.

The specific steps of measuring the parameters of the single-link Gamma characteristic model are as follows:

1. Select evenly spaced N sampling points on the interval [0, 1] of the input brightness signal L _in : Lin _in (0), Lin _in (1), Lin _in (2)...L _in (i)... …L _in (N-2), Lin _in (N-1);

2. Input the N sampling values of the luminance signal into the link respectively, and measure the corresponding values of the N actual output luminance signals: L ^P _out (0), L ^P _out (1), L ^P _out (2)...L ^P _out (i)...L ^P _out (N-2), L ^P _out (N-1);

3. The objective function of the construction fitting is that the objective function is related to the difference between the actual detected output brightness signal and the theoretical output brightness signal determined by the Gamma characteristic model, and the smaller the difference, the equivalent effect of the model is shown closer to the actual situation.

There are many ways to construct the objective function, and the more commonly used ones are the following formula 9 or formula 10:

$f_{T1}(p,\mathrm{\&alpha;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{\mathrm{PL}}_{\mathrm{in}}{\left(i\right)}^{\mathrm{\&alpha;}}-(1-p))}^2---\left(9\right)$ or,

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}^{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; QUR</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

4. Set the threshold T of the objective function value and the maximum number of iterations M, and use the mathematical optimization method to find the most suitable parameter group;

First, for the cost function of the first category $f_{T1}(p,\mathrm{\&alpha;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{\mathrm{PL}}_{\mathrm{in}}{\left(i\right)}^{\mathrm{\&alpha;}}-(1-p))}^2,$ Use some mathematical optimization technique, such as: hill climbing method, 0.618 method (Hua Luogeng optimization method), steepest descent method or conjugate gradient method to find its minimum value;

This process is actually an iterative process. In this process, the parameters p and α are constantly adjusted, and the function value F is continuously decreasing. When the function value decreases to less than the given threshold T, it is considered that the minimum point has been found. At this time, the corresponding parameters p and α are considered to be the real parameters of the application environment model. It should be noted that the value ranges of the parameters p and α are: 0<p≤1, α≥1;

if for $f_{T1}(p,\mathrm{\&alpha;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{\mathrm{PL}}_{\mathrm{in}}{\left(i\right)}^{\mathrm{\&alpha;}}-(1-p))}^2$ After M iterations, if the function cannot drop below the threshold T, it is considered that the model selection is wrong. The second type of model should be chosen, so for $f_{T2}(q,\mathrm{\&beta;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{({\mathrm{QUR}}_{\mathrm{in}}\left(i\right)+(1-q))}^{\frac{1}{\mathrm{\&beta;}}})}^2$ Repeat the above step 4 to obtain the corresponding model parameters q and β. It should be noted that the value ranges of the parameters p and α are respectively: q≥1 and β≥1.

If you want to get more accurate parameters, you can iterate a few more times after the objective function value F drops below the threshold T. If the objective function value F continues to decline, or rises after falling, or directly rises, regardless of the objective function value F is what kind of change, then choosing the parameter corresponding to the minimum value as the measurement result will improve the accuracy of parameter measurement to a certain extent.

It can be seen that the determination of the model type and the measurement of parameters are carried out at the same time. In practice, the types of equivalent models are not limited to these two forms. Through the above method, the most suitable method can be found in all relevant equivalent models by measuring parameters. one of.

The above method can also be used to measure the parameters of the multi-link comprehensive Gamma characteristic model. The specific steps are as follows:

1. Select evenly spaced N sampling points on the interval [0, 1] of the input brightness signal L _in : Lin _in (0), Lin _in (1), Lin _in (2)...L _in (i)... …L _in (N-2), Lin _in (N-1);

2. Input the N sampling values of the luminance signal into the link respectively, and measure the corresponding values of the N actual output luminance signals: L ^P _out (0), L ^P _out (1), L ^P _out (2)...L ^P _out (i)...L ^P _out (N-2), L ^P _out (N-1);

3. The objective function of the construction fitting is that the objective function is related to the difference between the actual detected output brightness signal and the theoretical output brightness signal determined by the Gamma characteristic model, and the smaller the difference, the equivalent effect of the model is shown closer to the actual situation.

There are many ways to construct the objective function, and the more commonly used ones are still Formula 9 or Formula 10:

$f_{T1}(p,\mathrm{\&alpha;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{\mathrm{PL}}_{\mathrm{in}}{\left(i\right)}^{\mathrm{\&alpha;}}-(1-p))}^2---\left(9\right)$ or,

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}^{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; QUR</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

4. Set the threshold T of the objective function value and the maximum number of iterations M, and use the mathematical optimization method to find the most suitable parameter group;

First, for the cost function of the first category $f_{T1}(p,\mathrm{\&alpha;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{\mathrm{PL}}_{\mathrm{in}}{\left(i\right)}^{\mathrm{\&alpha;}}-(1-p))}^2,$ Use some mathematical optimization technique, such as: hill climbing method, 0.618 method (Hua Luogeng optimization method), steepest descent method or conjugate gradient method to find its minimum value;

This process is actually an iterative process. In this process, the parameters p and α are constantly adjusted, and the function value F is continuously decreasing. When the function value decreases to less than the given threshold T, it is considered that the minimum point has been found. At this time, the corresponding parameters p and α are considered to be the real parameters of the application environment model. It should be noted that the difference from the single-link measurement is that the value ranges of the parameters p and α are: 0<p≤ 1. α≥0;

Likewise, if for $f_{T1}(p,\mathrm{\&alpha;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{\mathrm{PL}}_{\mathrm{in}}{\left(i\right)}^{\mathrm{\&alpha;}}-(1-p))}^2$ After M iterations, if the function cannot drop below the threshold T, it is considered that the model selection is wrong. The second type of model should be chosen, so for $f_{T2}(q,\mathrm{\&beta;})=\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(L_{\mathrm{out}}^P\left(i\right)-{({\mathrm{QUR}}_{\mathrm{in}}\left(i\right)+(1-q))}^{\frac{1}{\mathrm{\&beta;}}})}^2$ Repeat the above step 4 to obtain the corresponding model parameters q and β. It should be noted that the difference from single-stage measurement is that the value ranges of parameters p and α are: q≥1 and β≥0, respectively.

In the same way as the single-stage parameter measurement method, if you want to get more accurate parameters, you can iterate several times after the objective function value F drops below the threshold T. If the objective function value F continues to drop, or rises after falling, Or directly increase, regardless of the change of the objective function value F, selecting the parameter corresponding to the minimum value as the measurement result will improve the accuracy of parameter measurement to a certain extent.

The same as the single-link parameter measurement method, the determination of the model type and the measurement of the parameters are carried out at the same time. In practice, the types of equivalent models are not limited to these two forms. Through the above method, all relevant equivalent models can be measured by measuring parameters. method to find the most suitable one.

Embodiment 2, the correction method of multi-link Gamma characteristic:

Based on the measurement method of the gamma characteristic equivalent model parameters of the first embodiment, the functional relationship of the gamma characteristic equivalent model can be obtained, and the correction model can be constructed by using the inverse function of the function, and the single-link or multi-link gamma characteristic Correction is performed, and a detailed description will be given below taking a specific application in a multimedia information system as an example.

As shown in Figure 9, in general, a multimedia information system is cascaded with multiple Gamma links. For different situations, such as situation A and situation B, the links participating in the cascade in the figure are ever-changing. Generally, Link 1 is the camera/camera, and Link N _t (the last one) is the display. In theory, a correction link (circuit implementation or software implementation) can be inserted between any two links (including inserting a correction link at the front or at the end), but this may not be the case in practice. For example, a correction link cannot be inserted between the display frame memory Gamma link and the display LUT Gamma link mentioned earlier. Therefore, in general, there may be P correction link insertion points, also called correction points. The method of the present invention only needs to select one of the above P correction points and insert a correction link at this point to realize all Gamma corrections.

For case A: One embodiment of the present invention is to add a calibration link between the camera and the display frame memory.

For Case B: An embodiment of the invention is:

1. For the local video/image, a correction link is added between the camera and the display frame memory;

2. For remote video/image, a correction link is added between the decoder and the display frame memory;

3. For the self-loop video/image, add a correction link between the camera and the encoder, or add a correction link between the decoder and the display frame memory.

Starting from this correction point, the number of links in the front is N _a (link 1 to link _Na ), and the number of links in the back is N _p (links _Na +1 to N _t , there is a relationship between _Na +N _p = N _t established), there are two special cases as follows:

1) When N _a or N _p is equal to zero, it corresponds to a special case where a correction link is inserted at the front or at the end. At this time, the system is actually regarded as a multi-link integrated Gamma characteristic for correction;

2) When N _a or N _p is equal to 1, the camera/camera head or display is calibrated separately.

Let the cascaded integrated Gamma characteristic of Na links from link 1 to link Na be G _a (.), and the cascaded integrated Gamma characteristic of N _p links from link Na ₊ 1 to link N _t be G _p (.). Using this method, the model of the calibration link can be obtained conveniently. And it should be noted that the method is not limited to the models of each single link, or the comprehensive model adopts the first and second types of models of the present invention. For other models, if the analytical form (closed form) of the inverse function of G _a (.) and G _p (.) can be obtained mathematically, then the sub-link decomposition method of the present invention is applicable. And, the present invention is also applicable to the model of some other forms, such as adopting the model of data table form, for G _a (.), G _p (.) itself does not have analytical form (such as adopting the look-up table method to realize, of course its inverse function There is no analytical form). For the model itself exists in the form of a data table, then its inverse model is the inverse table of the data table. A table has two columns and many rows. The left column (input column) is the sampling value of the input signal, that is, the value to be corrected Signal value, the right column (output column) is the corresponding output signal value, that is, the corrected signal value, the number of rows depends on the number of sampling points, the more rows, the more accurate, the reverse table is the new data table obtained by swapping the left and right columns , an embodiment of the present invention is realized with look-up table exactly.

The specific correction method includes the following steps:

1. Establish the correction sub-link G _ac (.):

If G _a (.) belongs to the comprehensive first-class equivalent model:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}}^{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

Then the G _ac (.) model is:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 14</span>}<span\; class="notranslate">\; )</span>$

If G _a (.) belongs to the second type of comprehensive equivalent model:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 15</span>}<span\; class="notranslate">\; )</span>$

Then the G _ac (.) model is:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}}^{{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; )</span>$

2. Establish the correction sub-link G _pc (.);

If G _p (.) belongs to the comprehensive first-class equivalent model:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}}^{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 17</span>}<span\; class="notranslate">\; )</span>$

Then the G _pc (.) model is:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 18</span>}<span\; class="notranslate">\; )</span>$

If G _p (.) belongs to the comprehensive second-class equivalent model:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 19</span>}<span\; class="notranslate">\; )</span>$

Then the G _pc (.) model is:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}}^{{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; q</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 1</span>}{<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 20</span>}<span\; class="notranslate">\; )</span>$

3. Cascading two correction sub-links to form a correction link;

As shown in Figure 9, the correction link G _c (.) is obtained by cascading sub-links G _ac (.) and sub-links G _pc (.) in the order that G _ac (.) comes first and G _pc (.) follows.

After establishing the G _ac (.) and G _pc (.) models, the specific implementation of the cascade of the two has the following methods:

1) Direct calculation method: According to _the definition of function composition, the parameters of G c (.) are calculated according to the parameters of G _ac (.) and G _pc (.). Because the exponents α and β are not necessarily integers or reciprocals of integers, the functional form after compounding generally involves the generalized Newton binomial expansion of non-integer exponents, which contains infinite numbers. For the convenience of calculation, only the first few items can be intercepted, which is complicated and causes calculation errors. Then, the output correction signal is calculated in real time according to the composite model and the input luminance signal.

2), Two-step method: first calculate the correction result of G _ac (.), that is, the input brightness signal is first corrected by G _ac (.), and the correction result is used as the input of G _pc (.) for secondary correction. The secondary corrected luminance signal output by G _pc (.) is used as the final correction result.

3) Table look-up method: According to 1) or 2), calculate enough points on the value interval of the input brightness signal, and record the correction results as a look-up table. Then, when performing correction, for the input signal value to be corrected, the correction result is obtained by looking up the table. The more table entries, that is, the denser the sample collection, the more accurate the table lookup effect.

For video data with a large amount of data, the amount of calculation for real-time calculation is very large, and table lookup is the most practical method. The structure of the table generally includes two columns and multiple rows: the signal value to be corrected is in the left column, the corrected result is listed in the right column, and the number of rows depends on the number of sampled values. The method of looking up the table is to search in the left column of the table according to the value of the signal to be corrected, and if found, directly use the corresponding value in the right column as the result of the table lookup. If not found, use linear interpolation to calculate, assuming that the value of the signal to be corrected is a, which is located between b and c (c>a>b) two adjacent items in the left column, and the corresponding right list item of b and c is d , e, then the final table lookup result $f=\frac{c-a}{c-b}b+\frac{a-b}{c-b}c.$

The beneficial effects brought by the technical solution of the present invention are as follows:

The invention solves the problem of measurement and correction of Gamma characteristics which are ubiquitous in multimedia information systems, and provides a general correction method for the comprehensive Gamma characteristics of arbitrary multi-link cascading. This can greatly improve the user experience of the multimedia information system, attract users, enhance the market competitiveness of related products, and promote the accelerated popularization of new telecommunication services such as video telephony and video conferencing, providing telecom operators with broad business opportunities.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and equivalent technologies thereof, the present invention also intends to include these modifications and variations.

Claims (19)

1. A gamma correction method for correcting N included in a multimedia information system_tGamma characteristics of individual segments, characterized in that the method comprises the steps of:

determining a correction point that will correct said N_tEach link is divided into N before the correction point_aA link and N after the correction point_pA link, wherein: n is a radical of_a≥0、N_p≥0、N_a+N_p＝N_t；

Determining the equivalent of said N_aOne ringDetermining a first equivalent model of the joint gamma characteristic and a first inverse model thereof to be equivalent to the N_pA second equivalent model and a second inverse model of the gamma characteristic of each link;

constructing a calibration link model according to the first inverse model and the second inverse model, and determining the N by using the calibration link model_aThe last output signal of each link is corrected and input into the N_pAnd (5) carrying out each link.

2. The method of claim 1, wherein the representation function of the inverse model is the corresponding inverse function when the equivalent model takes the functional representation form.

3. The method of claim 2, wherein the determining the first equivalent model or the second equivalent model comprises the steps of:

a1, detecting N sampling values L of input signal respectively_in(i) Inputting N values L of the actual output signal generated by the link^P _out(i) Wherein: i is more than or equal to 0 and less than or equal to N-1;

a2, selecting a model to be tested from a group of alternative equivalent models in a function representation form to execute the step A3;

a3, selecting a group of initial parameters for the model to be tested;

a4, calculating:

calculating N values L of theoretical output signals corresponding to the N sampling values according to the model to be tested^M _out(i) Wherein: i is more than or equal to 0 and less than or equal to N-1; and

a value of the objective function F corresponding to each pair of L^P _out(i) And L^M _out(i) Is correlated with the difference of (a);

a5, judging whether the objective function value F is equal to or less than a set threshold value, if so, determining that the model to be tested is accepted as a final equivalent model, and switching to the step A8 after taking a parameter corresponding to the minimum objective function value F as a parameter of the equivalent model; otherwise, executing step A6;

a6, judging whether the execution times of the step A4 reach the limited iteration times, if so, selecting one alternative equivalent model from other undetected alternative equivalent models as a model to be tested and returning to the step A3; otherwise, executing step A7;

a7, adjusting the model parameters by using a mathematical optimization method, and returning to the step A4;

and A8, ending.

4. The method as claimed in claim 3, wherein in step A5, when the objective function value F is equal to or less than a set threshold value, the parameters are adjusted and the objective function value F is calculated by the mathematical optimization method according to a set number of cycles, and then the parameter corresponding to the smallest objective function value F is used as the parameter of the equivalent model.

5. The method of claim 3 or 4, further comprising the steps of:

respectively substituting the measured parameters into the expression functions of the corresponding inverse models, and solving a first inverse function formula corresponding to the first inverse model and a second inverse function formula corresponding to the second inverse model;

and constructing a correction model by using the first inverse function formula and the second inverse function formula.

6. The method of claim 5, wherein the method of constructing the calibration link model comprises one of:

direct calculation method: real-time computation of N using a complex function of a first inverse function and a second inverse function_aThe correction signal of the final output signal of each link;

the two-step calculation method comprises the following steps: real-time computation of N using a first inverse function_aCalculating a secondary correction signal of the primary correction signal by using a second inverse function, and taking the secondary correction signal as the correction signal;

table look-up method: according to the direct calculation in advanceBy calculation of said N_aAnd correcting values corresponding to a plurality of sampling values in the value interval of the final output signal of each link, storing the corresponding relation in a data table, and determining the correcting value of any value to be corrected by inquiring the data table in real time.

7. The method of claim 6, wherein the table lookup comprises:

when the input value to be corrected is in the data table, the corresponding correction value is directly obtained through table look-up;

when the input value to be corrected is not in the input column of the data table, the corresponding correction value is obtained by adopting a linear interpolation average method.

8. The method of claim 3, wherein the objective function value F satisfies the following condition:

<math> <mi>F</mi> </math><math> <mo>=</mo> </math><math> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msup> <mrow> <mo>(</mo> <msubsup> <mi>L</mi> <mi>out</mi> <mi>P</mi> </msubsup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>L</mi> <mi>out</mi> <mi>M</mi> </msubsup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>.</mo> </mrow> </math>

9. the method of claim 3, wherein the functional relation and its corresponding inverse functional relation of the set of alternative equivalent models in functional representation form comprises:

the equivalent model functional relation of the gamma characteristic is as follows: l is_out＝pL_in ^α+ (1-p), wherein: the domain of the function is the interval [0, 1]]The range is the interval [ (1-p), 1](ii) a Then it isThe inverse of the function is:

<math> <mrow> <msub> <mi>L</mi> <mi>out</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mn>1</mn> <mi>p</mi> </mfrac> <msub> <mi>L</mi> <mi>in</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mn>1</mn> <mi>p</mi> </mfrac> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mi>&alpha;</mi> </mfrac> </msup> <mo>;</mo> </mrow> </math> or,

the equivalent model functional relation of the gamma characteristic is as follows: <math> <mrow> <msub> <mi>L</mi> <mi>out</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>qL</mi> <mi>in</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mi>&beta;</mi> </mfrac> </msup> <mo>,</mo> </mrow> </math> wherein: the domain of the function is the interval [1-1/q, 1]The range is the interval [0, 1]](ii) a The inverse of the function is then:

<math> <mrow> <msub> <mi>L</mi> <mi>out</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mi>q</mi> </mfrac> <msup> <msub> <mi>L</mi> <mi>in</mi> </msub> <mi>&beta;</mi> </msup> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mn>1</mn> <mi>q</mi> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow> </math>

wherein: l is_inIs an input signal value, L_outRespectively taking the output signal values, p and alpha, and q and beta as parameters to be measured; and when N is_a1 or N_pWhen the value is 1: p is more than 0 and less than or equal to 1, alpha is more than or equal to 1, q is more than or equal to 1, and beta is more than or equal to 1; when N is present_a> 1 or N_pAt > 1: p is more than 0 and less than or equal to 1, alpha is more than or equal to 0, q is more than or equal to 1, and beta is more than or equal to 0.

10. The method of claim 3, wherein the mathematical optimization method is: hill climbing, or 0.618, or steepest descent, or conjugate gradient.

11. A method as claimed in claim 3, characterized in that N sample values of the input signal are selected in the interval [0, 1 ].

12. The method of claim 1, wherein when the equivalent model is in the form of a data table, the corresponding inverse model is an inverse table of the data table.

13. A detection method for determining a gamma characteristic equivalent model and parameters thereof, wherein the gamma characteristic equivalent model is used for gamma characteristics in an equivalent signal transmission or processing link, and is characterized by comprising the following steps:

b1, detecting N sampling values L of input signal respectively_in(i) Inputting N values L of the actual output signal generated by the link^P _out(i) Wherein: i is more than or equal to 0 and less than or equal to N-1;

b2, selecting one to-be-tested model from a group of alternative equivalent models in a function representation form to execute the step B3;

b3, selecting a group of initial parameters for the model to be tested;

b4, calculating:

calculating N values of theoretical output signals corresponding to the N sampling values of the signals according to the model to be tested: l is^M _out(i) Wherein: i is more than or equal to 0 and less than or equal to N-1; and

a value of the objective function F corresponding to each pair of L^P _out(i) And L^M _out(i) Is correlated with the difference of (a);

b5, judging whether the objective function value F is equal to or smaller than a set threshold value, if so, considering that the model to be tested is accepted as a final equivalent model, and switching to a step B8 after taking the group of parameters as the parameters of the equivalent model; otherwise, executing step B6;

b6, judging whether the execution times of the step B4 reach the limited iteration times, if so, selecting one alternative equivalent model from other undetected alternative equivalent models as a model to be tested and returning to the step B3; otherwise, executing step B7;

b7, adjusting the parameters by using the mathematical optimization method, and returning to the step B4;

and B8, ending.

14. The method as claimed in claim 13, wherein in step B5, when the objective function value F is equal to or less than a predetermined threshold value, the parameters are adjusted by the mathematical optimization method according to a predetermined number of cycles, and the objective function value F is calculated, and then the parameter corresponding to the smallest objective function value F is used as the parameter of the equivalent model.

15. The method according to claim 13 or 14, wherein the objective function value F satisfies the following condition:

<math> <mi>F</mi> </math><math> <mo>=</mo> </math><math> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msup> <mrow> <mo>(</mo> <msubsup> <mi>L</mi> <mi>out</mi> <mi>P</mi> </msubsup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>L</mi> <mi>out</mi> <mi>M</mi> </msubsup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>.</mo> </mrow> </math>

16. the method of claim 15, wherein the gamma characteristic comprises a single-ring-segment gamma characteristic or a multi-ring-cascade synthesis gamma characteristic.

17. The method of claim 15, wherein the functional relationships of the set of candidate equivalent models and their corresponding inverse functions comprise:

the equivalent model functional relation of the gamma characteristic is as follows: l is_out＝pL_in ^α+ (1-p), wherein: the domain of the function is the interval [0, 1]]The range is the interval [ (1-p), 1](ii) a The inverse of the function is then:

<math> <mrow> <msub> <mi>L</mi> <mi>out</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mn>1</mn> <mi>p</mi> </mfrac> <msub> <mi>L</mi> <mi>in</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mn>1</mn> <mi>p</mi> </mfrac> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mi>&alpha;</mi> </mfrac> </msup> <mo>;</mo> </mrow> </math> or,

the equivalent model functional relation of the gamma characteristic is as follows: <math> <mrow> <msub> <mi>L</mi> <mi>out</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>qL</mi> <mi>in</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mi>&beta;</mi> </mfrac> </msup> <mo>,</mo> </mrow> </math> wherein: the domain of the function is the interval [1-1/q, 1]The range is the interval [0, 1]](ii) a The inverse of the function is then:

<math> <mrow> <msub> <mi>L</mi> <mi>out</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mi>q</mi> </mfrac> <msup> <msub> <mi>L</mi> <mi>in</mi> </msub> <mi>&beta;</mi> </msup> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mn>1</mn> <mi>q</mi> </mfrac> <mo>)</mo> </mrow> <mo>;</mo> </mrow> </math>

wherein: l is_inIs an input signal value, L_outRespectively taking the output signal values, p and alpha, and q and beta as parameters to be measured; in the single-ring-segment gamma characteristic: p is more than 0 and less than or equal to 1, alpha is more than or equal to 1, q is more than or equal to 1, and beta is more than or equal to 1; the multi-ring gammaAmong the characteristics: p is more than 0 and less than or equal to 1, alpha is more than or equal to 0, q is more than or equal to 1, and beta is more than or equal to 0.

18. The method of claim 13, wherein the mathematical optimization method is: hill climbing, or 0.618, or steepest descent, or conjugate gradient.

19. The method of claim 13 wherein N samples of the input signal are selected in the interval [0, 1 ].

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