CN110233962B - Confidence optimization method and device and computer readable storage medium - Google Patents
Confidence optimization method and device and computer readable storage medium Download PDFInfo
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- CN110233962B CN110233962B CN201910345565.6A CN201910345565A CN110233962B CN 110233962 B CN110233962 B CN 110233962B CN 201910345565 A CN201910345565 A CN 201910345565A CN 110233962 B CN110233962 B CN 110233962B
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- H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/67—Focus control based on electronic image sensor signals
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
- H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
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Abstract
The application relates to a method and a device for optimizing confidence and a computer readable storage medium. The confidence degree optimization method comprises the following steps: acquiring a current noise compensation parameter; acquiring a first confidence coefficient, a second confidence coefficient and a third confidence coefficient according to the current noise compensation parameter; and performing weighting operation on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient to determine the confidence coefficient of the current noise compensation parameter. The confidence degree optimizing method provided by the invention has higher accuracy degree of calculated confidence degree, so that the confidence degree threshold value is more reasonable and accurate, and the problem that the confidence degree threshold value cannot be accurately calculated due to the fact that the confidence degree value of a certain noise compensation parameter is suddenly high, so that whether the confidence degree of the current scene is accurate or not cannot be accurately estimated can be solved.
Description
Technical Field
The present application relates to the field of terminal devices, and in particular, to a method and an apparatus for optimizing a confidence level, and a computer-readable storage medium.
Background
The Auto-Focus (Auto-Focus) aim is to control a motor to push a lens to the clearest imaging position to complete focusing, and a reasonable trigger mechanism is debugged to detect whether the focusing is optimal or not. PDAF (phase Detection Auto Focus) is a main focusing mode in Auto-Focus in the industry, the PDAF focusing does not need repeated movement of a lens, the focusing stroke is short, and the focusing process is clean and not hesitant. Therefore, the PDAF has the advantages of high focusing speed and high focusing precision, and becomes the mainstream focusing mode for the focusing of the mobile phone camera.
The PDAF focusing process is based on the defocus value and the confidence value as the basis for moving the lens. The defocus value is used as a scale for moving in the focusing clear direction, the theoretical defocus value is 0 when the focusing is clear, and the defocus value is larger if the focusing is more fuzzy. The confidence is an evaluation criterion for judging whether the focus value of the focusing scene is accurate or not under the current brightness. If the focusing scene brightness is higher, the confidence value is larger, otherwise, the focusing scene brightness is lower, and the confidence value is smaller. If the confidence value is lower than the value set by the debugger in the effect parameter, the defocus value at this time is inaccurate.
The PDAF detects the phase difference of the scene in different scenes through special PD pixels on the high-pass sensor, and converts the phase difference into a defocus value. The phase difference is converted to defocus by PDlib (high-pass PDlib) in the PDAF flow. At this time, the confidence value is also calculated according to the brightness of the current scene. The block diagram of the calculation is shown in fig. 1. The calculation process is completed by a high-pass algorithm, and the realization process is a closed process and is not easy to modify.
Because the algorithm of the confidence value is calculated by a closed algorithm, an effect debugger needs to set a reasonable judgment threshold confidence _ level, and if the result value of the current confidence is higher than the confidence _ level, the PD result is accurate. And if the result value of the current confidence is lower than the confidence _ level, the PD result is not accurate.
The current linear algorithm: the relevant parameters of confidence _ level are set by a debugging person of Auto-Focus, and the main parameters are a noise _ gain parameter and a confidence _ threshold value. These two parameters together define a linear function. Thus, a coordinate system of a plane can be formed by taking the noise _ gain parameter as an abscissa and the confidence _ threshold parameter as an ordinate. As shown schematically in fig. 2.
The calculation method is that the position in the parameter table is determined according to the current noise-gain value, for example, cur _ noise gain is 20, which is higher than 15 set by Tuning parameter, then a linear straight line is determined by two parameters less than cur _ noise gain, and the confidence threshold corresponding to cur _ noise gain is obtained according to the linear straight line, which is the point where the dotted line and the straight line meet.
The method is simple in calculation, and the calculation result cannot be very accurate: because the actual value of confidence is determined by the current value of cur _ noise _ gain each time, only two parameters participate in the fitting calculation of the linear function at the moment, the accuracy degree is limited, and particularly under the environment with severe scene change, the calculation of confidence _ level is inaccurate.
Disclosure of Invention
In order to solve the technical problem or at least partially solve the technical problem, the present application provides a method, an apparatus, and a computer-readable storage medium for confidence level optimization.
In a first aspect, the present application provides a method for optimizing confidence, including: acquiring a current noise compensation parameter; acquiring a first confidence coefficient, a second confidence coefficient and a third confidence coefficient according to the current noise compensation parameter; and performing weighting operation on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient to determine the confidence coefficient of the current noise compensation parameter.
In the above technical solution, preferably, the method for optimizing the confidence further includes: presetting a first weight, a second weight and a third weight; wherein the sum of the first weight, the second weight and the third weight is 1.
In any of the above technical solutions, preferably, the method for optimizing the confidence further includes: determining an interval where the current noise compensation parameter is located; equally dividing the interval into three subintervals; determining a subinterval in which the current noise compensation parameter is positioned; and determining the corresponding relation between the first confidence coefficient, the second confidence coefficient and the third confidence coefficient as well as the first weight, the second weight and the third weight according to the subinterval where the current noise compensation parameter is located.
In any of the above technical solutions, preferably, determining the correspondence between the first confidence level, the second confidence level, and the third confidence level and the first weight, the second weight, and the third weight according to the subinterval where the current noise compensation parameter is located specifically includes: the current noise compensation parameter is in a first subinterval, and the weight of the first confidence coefficient is determined to be a second weight, the weight of the second confidence coefficient is determined to be a third weight, and the weight of the third confidence coefficient is determined to be a first weight; the current noise compensation parameter is in a second subinterval, and the weight of the first confidence coefficient is determined as a first weight, the weight of the second confidence coefficient is determined as a third weight, and the weight of the third confidence coefficient is determined as a second weight; the current noise compensation parameter is in a third subinterval, and the weight of the first confidence coefficient is determined as a second weight, the weight of the second confidence coefficient is determined as a second weight, and the weight of the third confidence coefficient is determined as a third weight; and the left endpoint of the second subinterval is the right endpoint of the first subinterval, and the right endpoint of the second subinterval is the left endpoint of the third subinterval.
In any of the above technical solutions, preferably, the obtaining of the first confidence level according to the current noise compensation parameter specifically includes: determining a quadratic function curve based on a preset one-dimensional noise compensation parameter array and the confidence coefficient threshold of each noise compensation parameter; and determining a first confidence coefficient according to the current noise compensation parameter and the quadratic function curve.
In any of the above technical solutions, preferably, the obtaining of the second confidence level according to the current noise compensation parameter specifically includes: acquiring a first noise compensation parameter and a second noise compensation parameter which are adjacent to the current noise compensation parameter left and right; wherein the first noise compensation parameter is less than the second noise compensation parameter; determining a first linear straight line based on the first noise compensation parameter and the second noise compensation parameter; and determining a second confidence degree according to the current noise compensation parameter and the first linear straight line.
In any of the above technical solutions, preferably, the obtaining of the third confidence level according to the current noise compensation parameter specifically includes: acquiring a third noise compensation parameter which is smaller than the first noise compensation parameter and is adjacent to the first noise compensation parameter on the left side; determining a second linear straight line based on the first noise compensation parameter and the third noise compensation parameter; and determining a third confidence degree according to the current noise compensation parameter and the second linear straight line.
In any of the above technical solutions, preferably, the number of noise compensation parameters included in the one-dimensional noise compensation parameter array is less than or equal to 8; the value range of each noise compensation parameter is as follows: 0 to 35; the confidence threshold value of each noise compensation parameter has the following value range: 100 to 800.
In a second aspect, the present application provides a confidence level optimizing apparatus, including: a memory, a processor, and a computer program stored on the memory and executable on the processor; the processor, when executing the computer program, implements: acquiring a current noise compensation parameter; acquiring a first confidence coefficient, a second confidence coefficient and a third confidence coefficient according to the current noise compensation parameter; and performing weighting operation on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient to determine the confidence coefficient of the current noise compensation parameter.
In the above technical solution, preferably, the processor executes the computer program to further implement: : presetting a first weight, a second weight and a third weight; wherein the sum of the first weight, the second weight and the third weight is 1.
In any of the above technical solutions, preferably, the processor executing the computer program further implements: : determining an interval where the current noise compensation parameter is located; equally dividing the interval into three subintervals; determining a subinterval in which the current noise compensation parameter is positioned; and determining the corresponding relation between the first confidence coefficient, the second confidence coefficient and the third confidence coefficient as well as the first weight, the second weight and the third weight according to the subinterval where the current noise compensation parameter is located.
In any of the above technical solutions, preferably, the processor executes the computer program to determine, according to the subinterval where the current noise compensation parameter is located, the correspondence between the first confidence coefficient, the second confidence coefficient, and the third confidence coefficient, and the first weight, the second weight, and the third weight specifically includes: when the current noise compensation parameter is in the first subinterval, determining that the weight of the first confidence coefficient is a second weight, the weight of the second confidence coefficient is a third weight, and the weight of the third confidence coefficient is a first weight; when the current noise compensation parameter is in the second subinterval, determining that the weight of the first confidence coefficient is the first weight, the weight of the second confidence coefficient is the third weight, and the weight of the third confidence coefficient is the second weight; when the current noise compensation parameter is in the third subinterval, determining that the weight of the first confidence coefficient is the second weight, the weight of the second confidence coefficient is the second weight, and the weight of the third confidence coefficient is the third weight; and the left endpoint of the second subinterval is the right endpoint of the first subinterval, and the right endpoint of the second subinterval is the left endpoint of the third subinterval.
In any of the above technical solutions, preferably, the implementation of the processor executing the computer program to acquire the first confidence level according to the current noise compensation parameter specifically includes: determining a quadratic function curve based on a preset one-dimensional noise compensation parameter array and the confidence coefficient threshold of each noise compensation parameter; and determining a first intersection point of the current noise compensation parameter and the quadratic function curve, wherein the confidence coefficient corresponding to the first intersection point is the first confidence coefficient.
In any of the above technical solutions, preferably, the implementation of the processor executing the computer program to acquire the second confidence level according to the current noise compensation parameter specifically includes: acquiring a first noise compensation parameter and a second noise compensation parameter which are adjacent to the current noise compensation parameter left and right; wherein the first noise compensation parameter is less than the second noise compensation parameter; determining a first linear straight line based on the first noise compensation parameter and the second noise compensation parameter; and determining a second intersection point of the current noise compensation parameter and the first linear straight line, wherein the confidence coefficient corresponding to the second intersection point is a second confidence coefficient.
In any of the above technical solutions, preferably, the implementation of the processor executing the computer program to acquire the third confidence level according to the current noise compensation parameter specifically includes: acquiring a third noise compensation parameter which is smaller than the first noise compensation parameter and is adjacent to the first noise compensation parameter on the left side; determining a second linear straight line based on the first noise compensation parameter and the third noise compensation parameter; and determining a third intersection point of the current noise compensation parameter and the second linear straight line, wherein the confidence coefficient corresponding to the third intersection point is a third confidence coefficient.
In any of the above technical solutions, preferably, the number of noise compensation parameters included in the one-dimensional noise compensation parameter array is less than or equal to 8; the value range of each noise compensation parameter is as follows: 0 to 35; the confidence threshold value of each noise compensation parameter has the following value range: 100 to 800.
In a third aspect, the present application provides a computer-readable storage medium, on which a confidence optimization method program is stored, and when being executed by a processor, the confidence optimization method program implements the confidence optimization method according to any one of the above technical solutions.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the method provided by the embodiment of the application, a first confidence coefficient, a second confidence coefficient and a third confidence coefficient are obtained according to the current noise compensation parameter; the first confidence coefficient, the second confidence coefficient and the third confidence coefficient are subjected to weighting operation to determine the confidence coefficient of the current noise compensation parameter, so that the problem that the confidence coefficient threshold value cannot be accurately calculated due to the fact that the confidence coefficient value of a certain noise compensation parameter is suddenly high, and whether the confidence coefficient of the current scene is accurate or not cannot be accurately estimated can be solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a diagram illustrating a PDAF scaling process in the prior art;
FIG. 2 is a diagram illustrating a prior art method for calculating a current confidence threshold;
fig. 3 is a schematic diagram of a mobile terminal according to various embodiments of the present application;
fig. 4 is a schematic front view of a mobile terminal according to various embodiments of the present application;
fig. 5 is a schematic reverse side view of a mobile terminal according to various embodiments of the present application;
FIG. 6 is a flowchart illustrating a method for optimizing confidence according to an embodiment of the present disclosure;
FIG. 7 is a schematic flowchart of a confidence level optimization method according to another embodiment of the present disclosure;
FIG. 8 is a schematic flow chart diagram illustrating a confidence level optimization method according to yet another embodiment of the present application;
FIG. 9 is a schematic flow chart diagram illustrating a confidence level optimization method according to yet another embodiment of the present application;
FIG. 10 is a schematic block diagram of a confidence level optimization apparatus provided in one embodiment of the present application;
FIG. 11 is a schematic diagram of calculating a current confidence threshold according to an embodiment of the present application;
fig. 12 is a schematic illustration of interval equal divisions provided in an embodiment of the present application.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The terminal may be implemented in various forms. For example, the terminal described in the present invention may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a wearable device, a smart band, a pedometer, and the like, and a fixed terminal such as a Digital TV, a desktop computer, and the like.
The following description will be given by way of example of a mobile terminal, and it will be understood by those skilled in the art that the construction according to the embodiment of the present invention can be applied to a fixed type terminal, in addition to elements particularly used for mobile purposes.
Referring to fig. 3, which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present invention, the mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, a/V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 3 is not intended to be limiting of mobile terminals, and that a mobile terminal may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile terminal in detail with reference to fig. 3:
the radio frequency unit 101 may be configured to receive and transmit signals during information transmission and reception or during a call, and specifically, receive downlink information of a base station and then process the downlink information to the processor 110; in addition, the uplink data is transmitted to the base station. Typically, radio frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 101 can also communicate with a network and other devices through wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to GSM (Global System for Mobile communications), GPRS (General Packet Radio Service), CDMA2000(Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access), FDD-LTE (Frequency Division duplex Long Term Evolution), and TDD-LTE (Time Division duplex Long Term Evolution).
WiFi belongs to short-distance wireless transmission technology, and the mobile terminal can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 102, and provides wireless broadband internet access for the user. Although fig. 3 shows the WiFi module 102, it is understood that it does not belong to the essential constitution of the mobile terminal, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The audio output unit 103 may convert audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output as sound when the mobile terminal 100 is in a call signal reception mode, a call mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 103 may also provide audio output related to a specific function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, and the like.
The a/V input unit 104 is used to receive audio or video signals. The a/V input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, the Graphics processor 1041 Processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphic processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 may receive sounds (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, or the like, and may be capable of processing such sounds into audio data. The processed audio (voice) data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 101 in case of a phone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.
The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 1061 and/or a backlight when the mobile terminal 100 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.
The display unit 106 is used to display information input by a user or information provided to the user. The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 107 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect a touch operation performed by a user on or near the touch panel 1071 (e.g., an operation performed by the user on or near the touch panel 1071 using a finger, a stylus, or any other suitable object or accessory), and drive a corresponding connection device according to a predetermined program. The touch panel 1071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may include other input devices 1072. In particular, other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.
Further, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the processor 110 to determine the type of the touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although the touch panel 1071 and the display panel 1061 are shown in fig. 3 as two separate components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions of the mobile terminal, and is not limited herein.
The interface unit 108 serves as an interface through which at least one external device is connected to the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and external devices.
The memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 109 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 110 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 109 and calling data stored in the memory 109, thereby performing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The mobile terminal 100 may further include a power supply 111 (e.g., a battery) for supplying power to various components, and preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, so as to manage charging, discharging, and power consumption management functions via the power management system.
Although not shown in fig. 3, the mobile terminal 100 may further include a bluetooth module or the like, which is not described in detail herein. FIG. 4 is a schematic front view of a mobile terminal 100 according to various embodiments of the present application; fig. 5 is a rear view of the mobile terminal 100 according to various embodiments of the present application.
Based on the hardware structure of the mobile terminal, the invention provides various embodiments of the method.
In a first aspect, the present application provides a method for optimizing confidence.
FIG. 6 shows a flowchart of a confidence optimization method according to an embodiment of the present invention. The confidence degree optimization method comprises the following steps:
and step 206, performing weighted operation on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient to determine the confidence coefficient of the current noise compensation parameter.
According to the confidence coefficient optimization method provided by the embodiment of the invention, a first confidence coefficient, a second confidence coefficient and a third confidence coefficient are obtained according to the current noise compensation parameter; the first confidence coefficient, the second confidence coefficient and the third confidence coefficient are subjected to weighting operation to determine the confidence coefficient threshold value of the current noise compensation parameter, so that compared with the prior art, the weighted average method only adopts two known noise compensation parameters and a linear function to perform fitting operation, the accuracy degree is higher, the confidence coefficient threshold value is more reasonable and accurate, and the problem that whether the confidence coefficient of a current scene is accurate or not cannot be accurately estimated due to inaccurate calculation of the confidence coefficient threshold value caused by the fact that the confidence coefficient value of a certain noise compensation parameter is suddenly high can be solved.
FIG. 7 is a flow diagram illustrating a confidence level optimization method according to another embodiment of the invention. The confidence degree optimization method comprises the following steps:
wherein the sum of the first weight, the second weight and the third weight is 1;
and 308, performing weighted operation on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient according to the first weight, the second weight and the third weight to determine the confidence coefficient of the current noise compensation parameter.
In this embodiment, a first confidence, a second confidence and a third confidence are obtained through the current noise compensation parameter, and a weighting operation is performed on the first confidence, the second confidence and the third confidence according to a preset first weight, a preset second weight and a preset third weight, so as to determine a confidence threshold of the current noise compensation parameter, where the sum of the first weight, the second weight and the third weight is 1.
FIG. 8 is a flow diagram illustrating a confidence level optimization method according to yet another embodiment of the invention. The confidence degree optimization method comprises the following steps:
wherein the sum of the first weight, the second weight and the third weight is 1;
In this embodiment, after the current noise parameter is obtained, an interval where the current noise compensation parameter is located may be determined according to a preset noise parameter array, and corresponding relationships between the first confidence level, the second confidence level, and the third confidence level and the first weight, the second weight, and the third weight may be set according to a position of the interval where the current noise compensation parameter is located.
FIG. 9 is a flow diagram illustrating a confidence level optimization method according to yet another embodiment of the invention. The confidence degree optimization method comprises the following steps:
wherein the sum of the first weight, the second weight and the third weight is 1;
the left endpoint of the second subinterval is the right endpoint of the first subinterval, and the right endpoint of the second subinterval is the left endpoint of the third subinterval;
In this embodiment, the interval in which the current noise compensation parameter is located is divided into 3 equal parts, which are a first sub-interval, a second sub-interval, and a third sub-interval, where a left end point of the second sub-interval is a right end point of the first sub-interval, a right end point of the second sub-interval is a left end point of the third sub-interval, that is, the second sub-interval is located between the first sub-interval and the third sub-interval, and assuming that a left end point of the interval in which the current noise compensation parameter is located is x1 and a right end point is x2, the first sub-interval is closest to x1, the second sub-interval is a middle value, and the third sub-interval is an interval close to x 2. Setting corresponding relations between the first confidence coefficient, the second confidence coefficient and the third confidence coefficient and the first weight, the second weight and the third weight according to the subinterval where the current noise compensation parameter is located, wherein the corresponding relations specifically include:
if the current noise compensation parameter is in the first subinterval, which indicates that the value of the current noise compensation parameter is close to x1, the weight of the first confidence coefficient is made to be the second weight, the weight of the second confidence coefficient is made to be the third weight, and the weight of the third confidence coefficient is made to be the first weight;
if the current noise compensation parameter is in the second subinterval, which indicates that the value of the current noise compensation parameter is in the middle, the weight of the first confidence coefficient is the first weight, the weight of the second confidence coefficient is the third weight, and the weight of the third confidence coefficient is the second weight;
if the current noise compensation parameter is in the third sub-interval, which means that the value of the current noise compensation parameter is close to x2, the weight of the first confidence coefficient is the second weight, the weight of the second confidence coefficient is the second weight, and the weight of the third confidence coefficient is the third weight.
In another embodiment of the present invention, preferably, the obtaining the first confidence level according to the current noise compensation parameter specifically includes: determining a quadratic function curve based on a preset one-dimensional noise compensation parameter array and the confidence coefficient threshold of each noise compensation parameter; and determining a first confidence coefficient according to the current noise compensation parameter and the quadratic function curve.
In this embodiment, a plane coordinate system is formed based on a preset one-dimensional noise compensation parameter array and a confidence threshold of each noise compensation parameter in the array, with the noise compensation parameter as an abscissa and the confidence threshold parameter as an ordinate. Through three points on the coordinate system, a quadratic function curve can be calculated, and the confidence coefficient threshold value is an increasing trend, so the quadratic function curve is a curve with an upward opening and an upward trend. A confidence threshold corresponding to the current noise compensation parameter can be determined through a quadratic function curve, and the confidence threshold is the first confidence.
In another embodiment of the present invention, preferably, the obtaining the second confidence level according to the current noise compensation parameter specifically includes: acquiring a first noise compensation parameter and a second noise compensation parameter which are adjacent to the current noise compensation parameter left and right; wherein the first noise compensation parameter is less than the second noise compensation parameter; determining a first linear straight line based on the first noise compensation parameter and the second noise compensation parameter; and determining a second confidence degree according to the current noise compensation parameter and the first linear straight line.
In this embodiment, a plane coordinate system is formed based on a preset one-dimensional noise compensation parameter array and a confidence threshold of each noise compensation parameter in the array, with the noise compensation parameter as an abscissa and the confidence threshold parameter as an ordinate. The method comprises the steps of obtaining a first noise compensation parameter and a second noise compensation parameter which are adjacent to the current noise compensation parameter left and right, forming a first linear straight line according to the first noise compensation parameter and the second noise compensation parameter, and determining another confidence coefficient threshold value, namely a second confidence coefficient, corresponding to the current noise compensation parameter through the first linear straight line.
In another embodiment of the present invention, preferably, the obtaining of the third confidence level according to the current noise compensation parameter specifically includes: acquiring a third noise compensation parameter which is smaller than the first noise compensation parameter and is adjacent to the first noise compensation parameter on the left side; determining a second linear straight line based on the first noise compensation parameter and the third noise compensation parameter; and determining a second confidence degree according to the current noise compensation parameter and the second linear straight line.
In this embodiment, a plane coordinate system is formed based on a preset one-dimensional noise compensation parameter array and a confidence threshold of each noise compensation parameter in the array, with the noise compensation parameter as an abscissa and the confidence threshold parameter as an ordinate. And determining a second linear straight line through a third noise compensation parameter which is smaller than the first noise compensation parameter and is adjacent to the first noise compensation parameter on the left side and the first noise compensation parameter, and determining a second confidence coefficient threshold value, namely a third confidence coefficient, corresponding to the current noise compensation parameter through the second linear straight line.
In any of the above embodiments, preferably, the number of noise compensation parameters included in the one-dimensional noise compensation parameter array is less than or equal to 8; the value range of each noise compensation parameter is as follows: 0 to 35; the confidence threshold value of each noise compensation parameter has the following value range: 100 to 800.
In this embodiment, the noise compensation parameter is first set, typically a one-dimensional array ranging from 0 to 35, with no more than 8 array elements, e.g., {0,6,9,12,35 }. A confidence threshold is set for each element of the noise compensation parameter array, typically a one-dimensional array ranging from 100 to 800, in an amount consistent with the noise compensation parameter, for example: {280,320,350,400,600}. Thus, the noise compensation parameter can be used as an abscissa, and the confidence threshold parameter can be used as an ordinate, so as to form a plane coordinate system.
The specific embodiment provides a confidence optimization method, which specifically comprises the following steps:
first, a noise compensation parameter is set, typically a one-dimensional array ranging from 0 to 35, with no more than 8 array elements, e.g., {0,6,9,12,35 }. A confidence threshold is set for each element of the noise compensation parameter array, typically a one-dimensional array ranging from 100 to 800, in an amount consistent with the noise compensation parameter, for example: {280,320,350,400,600}. Thus, the noise compensation parameter can be used as an abscissa, and the confidence threshold parameter can be used as an ordinate, so as to form a plane coordinate system.
As shown in fig. 11, a quadratic function (e.g., curve Y in fig. 11) is calculated from three points, and since the quadratic function is a gradually increasing trend, the quadratic function is necessarily a curve with an upward opening and a rising trend. The intersection of the current-noise _ gain value and the quadratic function is the new increment point y1 (i.e., the first confidence).
A linear straight line (e.g. the straight line L2 in fig. 11) is determined by two parameters smaller than the current noise _ gain, and a confidence threshold corresponding to cur _ noise _ gain is determined from the linear straight line, which is the point y2 (i.e. the second confidence) where the dashed line and the straight line L2 meet.
The left and right parameters of the parameter point of current _ noise _ gain form a linear straight line (e.g., the straight line L1 in fig. 11), and the intersection point with noise _ gain _ current is y3 (i.e., the third confidence).
The values of y1, y2, y3 were obtained by the intersection of the curves. The confidence _ level corresponding to cur _ noise _ gain is solved through the calculation among y1, y2 and y 3. The calculation is as follows:
the y1, y2, y3 are weighted and summed by the current cur _ noise _ gain position. Setting the weights weight _ a, weight _ b and weight _ c as {0.4, 0.3 and 0.3}, respectively, and setting the relationships between the weight _ a (namely, the first weight), the weight _ b (namely, the second weight) and the weight _ c (namely, the third weight) and y1, y2 and y3 according to the position of the section where cur _ noise _ gain is located.
Dividing the Section of cur _ noise _ gain into 3 parts, as shown in FIG. 12, S1(Section1), S2(Section2), S3(Section 3); s1 is the closest interval to 15; s2 is a median; s3 is an interval near 22.
If the value of noise _ gain _ current is within S1, indicating that the value of noise _ gain _ current is close to 15, then let y2 multiply weight _ a, y1 multiply weight _ b, and y3 multiply weight _ c;
cur_confidence_level＝y2×weight_a+y1×weight_b+y3×weight_c。
if the value of noise _ gain _ current is within S, which means that the value of noise _ gain _ current is in the middle, then let y1 be multiplied by weight _ a, y2 be multiplied by weight _ b, and y3 be multiplied by weight _ c;
cur_confidence_level＝y1×weight_a+y2×weight_b+y3×weight_c；
if the value of noise _ gain _ current is in S3, indicating that the value of noise _ gain _ current is close to 22, then let y3 multiply weight _ a, y1 multiply weight _ b, and y2 multiply weight _ c;
cur_confidence_level＝y3×weight_a+y1×weight_b+y2×weight_c。
by the weighted average method, the situation _ level threshold generated due to the fact that the situation value of a certain noise _ gain parameter is suddenly high can be improved, and the situation _ level threshold cannot be accurately calculated, so that whether the situation of the current scene is accurate or not can not be accurately estimated.
In a second aspect, the present application provides a confidence level optimizing apparatus 600, as shown in fig. 10, where the confidence level optimizing apparatus 600 includes: memory 602, processor 604, and computer programs stored on memory 602 and executable on processor 604; the processor 604, when executing the computer program, implements: acquiring a current noise compensation parameter; acquiring a first confidence coefficient, a second confidence coefficient and a third confidence coefficient according to the current noise compensation parameter; and performing weighting operation on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient to determine the confidence coefficient of the current noise compensation parameter.
According to the confidence optimization device 600 provided by the embodiment of the invention, a first confidence, a second confidence and a third confidence are obtained according to the current noise compensation parameters; the first confidence coefficient, the second confidence coefficient and the third confidence coefficient are subjected to weighting operation to determine the confidence coefficient threshold value of the current noise compensation parameter, so that compared with the prior art, the weighted average method only adopts two known noise compensation parameters and a linear function to perform fitting operation, the accuracy degree is higher, the confidence coefficient threshold value is more reasonable and accurate, and the problem that whether the confidence coefficient of a current scene is accurate or not cannot be accurately estimated due to inaccurate calculation of the confidence coefficient threshold value caused by the fact that the confidence coefficient value of a certain noise compensation parameter is suddenly high can be solved.
In one embodiment of the present invention, preferably, the processor 604 executing the computer program further realizes: : presetting a first weight, a second weight and a third weight; wherein the sum of the first weight, the second weight and the third weight is 1.
In this embodiment, a first confidence, a second confidence and a third confidence are obtained through the current noise compensation parameter, and a weighting operation is performed on the first confidence, the second confidence and the third confidence according to a preset first weight, a preset second weight and a preset third weight, so as to determine a confidence threshold of the current noise compensation parameter, where the sum of the first weight, the second weight and the third weight is 1.
In one embodiment of the present invention, preferably, the processor 604 executing the computer program further realizes: : determining an interval where the current noise compensation parameter is located; equally dividing the interval into three subintervals; determining a subinterval in which the current noise compensation parameter is positioned; and determining the corresponding relation between the first confidence coefficient, the second confidence coefficient and the third confidence coefficient as well as the first weight, the second weight and the third weight according to the subinterval where the current noise compensation parameter is located.
In this embodiment, after the current noise parameter is obtained, an interval where the current noise compensation parameter is located may be determined according to a preset noise parameter array, and corresponding relationships between the first confidence level, the second confidence level, and the third confidence level and the first weight, the second weight, and the third weight may be set according to a position of the interval where the current noise compensation parameter is located.
In an embodiment of the present invention, preferably, the processor 604 executes a computer program to determine, according to the subinterval where the current noise compensation parameter is located, correspondence between the first confidence level, the second confidence level, and the third confidence level, and the first weight, the second weight, and the third weight specifically: when the current noise compensation parameter is in the first subinterval, determining that the weight of the first confidence coefficient is a second weight, the weight of the second confidence coefficient is a third weight, and the weight of the third confidence coefficient is a first weight; when the current noise compensation parameter is in the second subinterval, determining that the weight of the first confidence coefficient is the first weight, the weight of the second confidence coefficient is the third weight, and the weight of the third confidence coefficient is the second weight; when the current noise compensation parameter is in the third subinterval, determining that the weight of the first confidence coefficient is the second weight, the weight of the second confidence coefficient is the second weight, and the weight of the third confidence coefficient is the third weight; and the left endpoint of the second subinterval is the right endpoint of the first subinterval, and the right endpoint of the second subinterval is the left endpoint of the third subinterval.
In this embodiment, the interval in which the current noise compensation parameter is located is divided into 3 equal parts, which are a first sub-interval, a second sub-interval, and a third sub-interval, where a left end point of the second sub-interval is a right end point of the first sub-interval, a right end point of the second sub-interval is a left end point of the third sub-interval, that is, the second sub-interval is located between the first sub-interval and the third sub-interval, and assuming that a left end point of the interval in which the current noise compensation parameter is located is x1 and a right end point is x2, the first sub-interval is closest to x1, the second sub-interval is a middle value, and the third sub-interval is an interval close to x 2. Setting corresponding relations between the first confidence coefficient, the second confidence coefficient and the third confidence coefficient and the first weight, the second weight and the third weight according to the subinterval where the current noise compensation parameter is located, wherein the corresponding relations specifically include:
if the current noise compensation parameter is in the first subinterval, which indicates that the value of the current noise compensation parameter is close to x1, the weight of the first confidence coefficient is made to be the second weight, the weight of the second confidence coefficient is made to be the third weight, and the weight of the third confidence coefficient is made to be the first weight;
if the current noise compensation parameter is in the second subinterval, which indicates that the value of the current noise compensation parameter is in the middle, the weight of the first confidence coefficient is the first weight, the weight of the second confidence coefficient is the third weight, and the weight of the third confidence coefficient is the second weight;
if the current noise compensation parameter is in the third sub-interval, which means that the value of the current noise compensation parameter is close to x2, the weight of the first confidence coefficient is the second weight, the weight of the second confidence coefficient is the second weight, and the weight of the third confidence coefficient is the third weight.
In an embodiment of the present invention, preferably, the processor 604 executes a computer program to obtain the first confidence level according to the current noise compensation parameter specifically: determining a quadratic function curve based on a preset one-dimensional noise compensation parameter array and the confidence coefficient threshold of each noise compensation parameter; and determining a first confidence coefficient according to the current noise compensation parameter and the quadratic function curve.
In this embodiment, a plane coordinate system is formed based on a preset one-dimensional noise compensation parameter array and a confidence threshold of each noise compensation parameter in the array, with the noise compensation parameter as an abscissa and the confidence threshold parameter as an ordinate. Through three points on the coordinate system, a quadratic function curve can be calculated, and the confidence coefficient threshold value is an increasing trend, so the quadratic function curve is a curve with an upward opening and an upward trend. A confidence threshold corresponding to the current noise compensation parameter can be determined through a quadratic function curve, and the confidence threshold is the first confidence.
In an embodiment of the present invention, preferably, the processor 604 executes a computer program to obtain the second confidence level according to the current noise compensation parameter specifically: acquiring a first noise compensation parameter and a second noise compensation parameter which are adjacent to the current noise compensation parameter left and right; wherein the first noise compensation parameter is less than the second noise compensation parameter; determining a first linear straight line based on the first noise compensation parameter and the second noise compensation parameter; and determining a second confidence degree according to the current noise compensation parameter and the first linear straight line.
In this embodiment, a plane coordinate system is formed based on a preset one-dimensional noise compensation parameter array and a confidence threshold of each noise compensation parameter in the array, with the noise compensation parameter as an abscissa and the confidence threshold parameter as an ordinate. The method comprises the steps of obtaining a first noise compensation parameter and a second noise compensation parameter which are adjacent to the current noise compensation parameter left and right, forming a first linear straight line according to the first noise compensation parameter and the second noise compensation parameter, and determining another confidence coefficient threshold value, namely a second confidence coefficient, corresponding to the current noise compensation parameter through the first linear straight line.
In an embodiment of the present invention, preferably, the processor 604 executes a computer program to obtain the third confidence level according to the current noise compensation parameter specifically: acquiring a third noise compensation parameter which is smaller than the first noise compensation parameter and is adjacent to the first noise compensation parameter on the left side; determining a second linear straight line based on the first noise compensation parameter and the third noise compensation parameter; and determining a second confidence degree according to the current noise compensation parameter and the second linear straight line.
In this embodiment, a plane coordinate system is formed based on a preset one-dimensional noise compensation parameter array and a confidence threshold of each noise compensation parameter in the array, with the noise compensation parameter as an abscissa and the confidence threshold parameter as an ordinate. And determining a second linear straight line through a third noise compensation parameter which is smaller than the first noise compensation parameter and is adjacent to the first noise compensation parameter on the left side and the first noise compensation parameter, and determining a second confidence coefficient threshold value, namely a third confidence coefficient, corresponding to the current noise compensation parameter through the second linear straight line.
In one embodiment of the present invention, preferably, the number of noise compensation parameters included in the one-dimensional noise compensation parameter array is less than or equal to 8; the value range of each noise compensation parameter is as follows: 0 to 35; the confidence threshold value of each noise compensation parameter has the following value range: 100 to 800.
In this embodiment, the noise compensation parameter is first set, typically a one-dimensional array ranging from 0 to 35, with no more than 8 array elements, e.g., {0,6,9,12,35 }. A confidence threshold is set for each element of the noise compensation parameter array, typically a one-dimensional array ranging from 100 to 800, in an amount consistent with the noise compensation parameter, for example: {280,320,350,400,600}. Thus, the noise compensation parameter can be used as an abscissa, and the confidence threshold parameter can be used as an ordinate, so as to form a plane coordinate system.
In a third aspect, the present application provides a computer-readable storage medium, wherein a confidence optimization method program is stored on the computer-readable storage medium, and when executed by a processor, the confidence optimization method program implements the confidence optimization method according to any one of the above embodiments. Therefore, the computer-readable storage medium has all the advantages of the confidence optimization method according to any of the above embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
In the description herein, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance unless explicitly stated or limited otherwise; the terms "connected," "mounted," "secured," and the like are to be construed broadly and include, for example, fixed connections, removable connections, or integral connections; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method for optimizing confidence, comprising:
acquiring a current noise compensation parameter of a focusing scene;
acquiring a first confidence coefficient according to confidence coefficient thresholds of all noise compensation parameters in a preset one-dimensional noise compensation parameter array and the current noise compensation parameter;
acquiring a second confidence coefficient according to a confidence coefficient threshold value of a noise compensation parameter adjacent to the current noise compensation parameter in a preset one-dimensional noise compensation parameter array and the current noise compensation parameter;
acquiring a third confidence coefficient according to a confidence coefficient threshold value of a noise compensation parameter adjacent to the left of the current noise compensation parameter in a preset one-dimensional noise compensation parameter array and the current noise compensation parameter;
and performing weighting operation on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient so as to determine the confidence coefficient of the current noise compensation parameter.
2. The method for optimizing confidence of claim 1, further comprising:
presetting a first weight, a second weight and a third weight;
wherein a sum of the first weight, the second weight, and the third weight is 1.
3. The confidence level optimization method of claim 2, further comprising:
determining an interval where the current noise compensation parameter is located;
equally dividing the interval into three subintervals;
determining the subinterval in which the current noise compensation parameter is located;
and determining the corresponding relations between the first confidence coefficient, the second confidence coefficient and the third confidence coefficient and the first weight, the second weight and the third weight according to the subintervals where the current noise compensation parameters are located.
4. The method for optimizing the confidence level according to claim 3, wherein the determining the correspondence between the first confidence level, the second confidence level, and the third confidence level and the first weight, the second weight, and the third weight according to the subinterval where the current noise compensation parameter is located specifically includes:
the current noise compensation parameter is in a first subinterval, and the weight of the first confidence coefficient is determined to be a second weight, the weight of the second confidence coefficient is determined to be a third weight, and the weight of the third confidence coefficient is determined to be a first weight;
the current noise compensation parameter is in a second subinterval, and the weight of the first confidence coefficient is determined to be a first weight, the weight of the second confidence coefficient is determined to be a third weight, and the weight of the third confidence coefficient is determined to be a second weight;
the current noise compensation parameter is in a third subinterval, and the weight of the first confidence coefficient is determined to be a second weight, the weight of the second confidence coefficient is determined to be a second weight, and the weight of the third confidence coefficient is determined to be a third weight;
and the left endpoint of the second subinterval is the right endpoint of the first subinterval, and the right endpoint of the second subinterval is the left endpoint of the third subinterval.
5. The method for optimizing the confidence level according to any one of claims 1 to 4, wherein the obtaining the first confidence level according to the confidence level threshold values of all the noise compensation parameters in the preset one-dimensional noise compensation parameter array and the current noise compensation parameter specifically includes:
determining a quadratic function curve based on a preset one-dimensional noise compensation parameter array and the confidence coefficient threshold of each noise compensation parameter;
and determining the first confidence coefficient according to the current noise compensation parameter and the quadratic function curve.
6. The method for optimizing the confidence level according to claim 5, wherein the obtaining a second confidence level according to the confidence level threshold of the noise compensation parameter adjacent to the current noise compensation parameter in the preset one-dimensional noise compensation parameter array and the current noise compensation parameter specifically includes:
acquiring a first noise compensation parameter and a second noise compensation parameter which are adjacent to the current noise compensation parameter left and right;
wherein the first noise compensation parameter is less than the second noise compensation parameter;
determining a first linear straight line based on the first noise compensation parameter and the second noise compensation parameter;
and determining the second confidence degree according to the current noise compensation parameter and the first linear straight line.
7. The method for optimizing the confidence level according to claim 6, wherein the obtaining a third confidence level according to the confidence level threshold of the noise compensation parameter adjacent to the current noise compensation parameter on the left in the preset one-dimensional noise compensation parameter array and the current noise compensation parameter specifically includes:
acquiring a third noise compensation parameter which is smaller than the first noise compensation parameter and is adjacent to the first noise compensation parameter on the left side;
determining a second linear straight line based on the first noise compensation parameter and the third noise compensation parameter;
determining the third confidence level according to the current noise compensation parameter and the second linear straight line.
8. The method of confidence optimization according to claim 5,
the number of the noise compensation parameters contained in the one-dimensional noise compensation parameter array is less than or equal to 8;
the value range of each noise compensation parameter is as follows: 0 to 35;
the value range of the confidence coefficient threshold value of each noise compensation parameter is as follows: 100 to 800.
9. An apparatus for optimizing confidence, comprising:
a memory, a processor, and a computer program stored on the memory and executable on the processor;
the computer program, when being executed by the processor, carries out the steps of the method of confidence optimization according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a confidence optimization method program that, when executed by a processor, implements the confidence optimization method according to any one of claims 1 to 8.
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