Processing method, processing device, and storage medium

Abstract

The present application provides a processing method, a processing device, and a storage medium. The processing method can be applied to a processing device, and comprises: determining or obtaining a candidate prediction mode list on the basis of a prediction mode, and predicting a current block on the basis of the candidate prediction mode list to determine or obtain a predicted block. The present application can reduce the prediction costs of predicted blocks, thereby improving the video coding and / or decoding efficiency.

Description

Processing method, processing device and storage medium This application claims the priority of the Chinese patent application filed with the China Patent Office on August 2, 2023, with application number 202310960404.4 and invention name “Processing Method, Processing Equipment and Storage Medium”, all contents of which are incorporated by reference in this application. Technical Field The present application relates to the technical field of image processing, and in particular to a processing method, a processing device and a storage medium. Background Art The existing high-efficiency video coding standard protocol (H.266 / VVC) proposes a video frame encoding technology to improve the encoding performance without significantly increasing the computational complexity. Specifically, when encoding and decoding the video frames, the protocol divides each frame into different blocks, performs prediction processing, and then performs encoding processing. In the process of conceiving and implementing the present application, the inventors discovered that there are at least the following problems: when performing prediction processing, especially cross-component prediction processing, due to the large number of cross-component prediction modes, the bit cost used to represent the mode is very high, which makes the cost of obtaining the prediction block higher, thereby limiting the encoding and decoding performance and / or reducing the encoding and decoding efficiency. The preceding description is intended to provide general background information and does not necessarily constitute prior art. Technical Solutions In response to the above technical problems, the present application provides a processing method, a processing device and a storage medium, aiming to solve the technical problem of how to reduce the prediction cost of the prediction block and thereby improve the efficiency of video encoding and / or decoding. The present application provides a processing method, which can be applied to a processing device, comprising the steps of: S10: Determine or obtain a candidate prediction mode list according to the prediction mode; S20: Predict the current block according to the candidate prediction mode list to determine or obtain a predicted block. Optionally, the step S10 includes at least one of the following: Creating or updating a candidate prediction mode list according to at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode; Determine or obtain a candidate prediction mode list of the current chrominance component according to the prediction mode of the luminance component of the neighbor block and the prediction mode of the non-neighbor block; Determine or obtain a candidate prediction mode list of the current chrominance component according to the prediction mode of the luminance component of the neighbor block, the prediction module of the non-neighbor block and the time domain prediction mode; Creating or updating a candidate prediction mode list according to a default prediction mode, a temporal prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode; Determine or obtain a candidate prediction mode list of the current chrominance component according to the luminance component prediction mode of the adjacent block and the prediction mode of the non-adjacent block; Determine or obtain a candidate prediction mode list of the current chrominance component according to the luminance component prediction mode of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode; Determine at least one prediction mode according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixels, and create or update a candidate prediction mode list according to the at least one prediction mode; Determine at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode and a time domain prediction mode according to the neighbor block pixels, and create or update a candidate prediction mode list according to the at least one prediction mode; A candidate prediction mode list is created or updated according to at least one prediction mode among a time domain prediction mode for obtaining displacement / offset of a motion vector and a prediction mode based on a historical record. Optionally, determining at least one prediction mode according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixel includes at least one of the following: Calculate SAD values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode according to neighbor block pixels, and determine at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the SAD values; Calculate cost values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode according to neighbor block pixels, and determine at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the cost values; When the cost mark obtained in the bitstream is a first value, first generation values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode are calculated according to neighbor block pixels, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode is determined according to the first generation values; Obtaining a cost tag in a bitstream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, calculating a cost value of pixels of a neighboring block according to the selected first cost calculation method or the second cost calculation method, and determining at least one prediction mode among the default prediction mode, the neighboring block prediction mode, and the non-neighboring block prediction mode according to the cost value; At least two different third-generation values ​​corresponding to the default prediction mode are calculated according to the neighbor block pixels, at least two different third-generation values ​​corresponding to the neighbor block prediction mode are calculated, and at least two different third-generation values ​​corresponding to the non-neighbor block prediction mode are calculated; a fourth-generation value is determined or obtained according to the at least two different third-generation values; and at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode is determined according to the fourth-generation value. Optionally, the creating or updating the candidate prediction mode list according to at least one prediction mode includes at least one of the following: adding at least one prediction mode to a preset prediction mode list, and creating or updating a candidate prediction mode list; Sorting the determined at least one prediction mode according to a preset sequence order, and creating or updating a candidate prediction mode list; A part or all of the prediction mode lists are determined according to at least one prediction mode, and a candidate prediction mode list is created or updated according to the part or all of the prediction mode lists. Optionally, determining part or all of the prediction mode list according to at least one prediction mode includes at least one of the following: Calculating the cost of at least one prediction mode according to the calculation rule, and determining part or all of the prediction mode list according to the cost of at least one prediction mode; Inputting at least one prediction mode into a neural network to obtain an output result, and determining part or all of the prediction mode list according to the output result; Inputting at least one prediction mode into a query table for querying to obtain a query result, and determining a partial or complete prediction mode list according to the query result; Performing pattern matching according to at least one prediction pattern to determine a partial or complete prediction pattern list; At least one prediction mode is input into the mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the candidate prediction mode list includes at least one of the following: List of first level patterns; Second level pattern list; List of first level chroma prediction modes; List of second level chroma prediction modes. Optionally, the creating or updating the candidate prediction mode list according to at least one prediction mode includes at least one of the following: Creating or updating a first-level pattern list according to at least one prediction mode, and determining or obtaining a second-level pattern list according to the first-level pattern list and other prediction modes outside the first-level pattern list; According to at least one prediction mode, a first-level chroma mode list is created or updated, and according to the first-level chroma mode list and other prediction modes outside the first-level chroma mode list, a second-level chroma mode list is determined or obtained. Optionally, the step S20 includes: Determining at least one target prediction mode in the candidate prediction mode list; The current block is predicted according to the at least one target prediction mode to determine or obtain a prediction block. Optionally, the method further comprises: The index corresponding to the target prediction mode and the prediction block are encoded. Optionally, the method further comprises: A decoding block is determined or obtained according to the prediction block. The present application also provides a processing device, comprising: The prediction module is used to determine or obtain a candidate prediction mode list according to the prediction mode, predict the current block according to the candidate prediction mode list, and determine or obtain a prediction block. The present application also provides a processing device, including: a memory and a processor, wherein a processing program is stored on the memory, and when the processing program is executed by the processor, the steps of any of the processing methods described above are implemented. The processing device mentioned in the present application can be a smart terminal, a server, etc. The present application also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the processing methods described above are implemented. As described above, the processing method of the present application can be applied to a processing device, including the steps of: determining or obtaining a candidate prediction mode list according to a prediction mode, predicting the current block according to the candidate prediction mode list, and determining or obtaining a prediction block. Through the above technical solution, it is possible to avoid the phenomenon that when the number of prediction modes is large, the prediction mode is not reasonably planned to improve the hit rate, resulting in a high prediction cost. By first determining or obtaining a candidate prediction mode list according to the prediction mode, so that the prediction mode that may be used for the current block can be reasonably planned into the candidate prediction mode list, and then predicting the current block through the candidate prediction mode list, determining or obtaining a prediction block, thereby reducing the mode representation cost of the prediction mode for predicting the current block, reducing the prediction cost of the prediction block, and thereby improving the efficiency of video encoding and / or decoding. BRIEF DESCRIPTION OF THE DRAWINGS The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. FIG1 is a schematic diagram of the hardware structure of a mobile terminal for implementing various embodiments of the present application; FIG2 is a diagram of a communication network system architecture provided in an embodiment of the present application; FIG3 is a schematic flow chart of a processing method according to the first embodiment; FIG4 is a schematic diagram of a current block and its neighbor and non-neighbor blocks in a processing method according to the first embodiment; 5 is a schematic diagram of creating / updating a mode list in a processing method according to the first embodiment; FIG6 is a schematic diagram of a scene of a current frame in a processing method according to the first embodiment; 7 is a schematic diagram of a scene of a Y component of a current frame in a processing method according to the first embodiment; FIG8 is a schematic diagram of a scene of a U component of a current frame in a processing method according to the first embodiment; FIG9 is a schematic diagram of a scene of a V component of a current frame in a processing method according to the first embodiment; FIG10 is a schematic flow chart of a processing method according to a second embodiment; FIG11 is a schematic diagram of a processing method based on SAD calculation according to the second embodiment; FIG12 is a schematic flow chart of a processing method according to a third embodiment; 13 is a schematic diagram of determining or generating a candidate prediction mode list based on a neural network in a processing method according to a third embodiment; 14 is a schematic diagram of determining or generating a candidate prediction mode list based on a query table in a processing method according to a third embodiment; 15 is a schematic diagram of determining or generating a candidate prediction mode list based on a mathematical model in a processing method according to a third embodiment; FIG16 is a schematic flow chart of a processing method according to a fourth embodiment; FIG17 is a schematic flow chart of a processing method according to a fifth embodiment; FIG18 is a schematic flow chart of a processing method according to a sixth embodiment; 19 is a schematic diagram of a decoding side determining a prediction mode in a processing method according to a sixth embodiment; FIG20 is another schematic diagram of a decoding side determining a prediction mode in a processing method according to the sixth embodiment; FIG. 21 is a schematic diagram of a module according to a processing device in an embodiment of the present application. The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Implementation Methods of the Application Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context in the specific embodiment. It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising" and "including" indicate the presence of the described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or", "and / or", "including at least one of the following" etc. used in this application can be interpreted as inclusive, or mean any one or any combination. For example, “comprising at least one of the following: A, B, C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”, and for another example, “A, B or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”. An exception to this definition will only occur when a combination of elements, functions, steps or operations are inherently mutually exclusive in some manner. It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps. As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context. It should be noted that in this article, step codes such as S10 and S20 are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step, those skilled in the art may execute S20 first and then S10, etc., but these should all be within the scope of protection of this application. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner. The processing device in this application may be a smart terminal or a server. The smart terminal may be implemented in various forms. For example, the smart terminal described in this application may include smart terminals such as mobile phones, tablet computers, laptop computers, PDAs, portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, and fixed terminals such as digital TVs and desktop computers. The following description will be made by taking a mobile terminal as an example, and those skilled in the art will understand that, in addition to components specifically used for mobile purposes, the construction according to the embodiments of the present application can also be applied to fixed-type terminals. Please refer to FIG1, which is a schematic diagram of the hardware structure of a mobile terminal for implementing various embodiments of the present application. The mobile terminal 100 may include: an RF (Radio Frequency) unit 101, a WiFi module 102, an audio output unit 103, an A / V (audio / video) input unit 104, a sensor 105, a display unit 106, a user input unit 107, an interface unit 108, a memory 109, a processor 110, and a power supply 111. Those skilled in the art will appreciate that the mobile terminal structure shown in FIG1 does not constitute a limitation on the mobile terminal, and the mobile terminal may include more or fewer components than shown, or combine certain components, or arrange the components differently. The following is a detailed introduction to the various components of the mobile terminal in conjunction with Figure 1: The radio frequency unit 101 can be used for receiving and sending signals during information transmission or calls. Specifically, after receiving the downlink information of the base station, it is sent to the processor 110 for processing; in addition, the uplink data is sent to the base station. Usually, the 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, etc. In addition, the radio frequency unit 101 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication), 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 Duplexing-Long Term Evolution, Frequency Division Duplexing-Long Term Evolution). Time Division Duplexing-Long Term Evolution (TDD-LTE), 5G and 6G, etc. WiFi is a short-range wireless transmission technology. The mobile terminal can help users send and receive emails, browse web pages, and access streaming media through the WiFi module 102, which provides users with wireless broadband Internet access. Although FIG1 shows the WiFi module 102, it is understandable that it is not a necessary component of the mobile terminal and can be omitted as needed without changing the essence of the invention. The audio output unit 103 can convert the audio data received by the RF unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output it as sound when the mobile terminal 100 is in a call signal reception mode, a talk mode, a recording mode, a voice recognition mode, a broadcast reception mode, etc. Moreover, the audio output unit 103 can also provide audio output related to a specific function performed by the mobile terminal 100 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, etc. The A / V input unit 104 is used to receive audio or video signals. The A / V input unit 104 may include a graphics processor (GPU) 1041 and a microphone 1042, and the graphics processor 1041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 106. The image frame processed by the graphics processor 1041 can be stored in the memory 109 (or other storage medium) or sent via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 can receive sound (audio data) via the microphone 1042 in a telephone call mode, a recording mode, a voice recognition mode, and other operating modes, and can process such sound into audio data. The processed audio (voice) data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 101 in the case of a telephone call mode. The microphone 1042 can implement various types of noise elimination (or suppression) algorithms to eliminate (or suppress) noise or interference generated in the process of receiving and sending audio signals. The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Optionally, the light sensor includes an ambient light sensor and a proximity sensor. Optionally, the ambient light sensor can adjust the brightness of the display panel 1061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 1061 and / or the backlight when the mobile terminal 100 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that can be configured on the mobile phone, such as fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here. The display unit 106 is used to display information input by the user or information provided to the user. The display unit 106 may include a display panel 1061, which 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 can be used to receive input digital or character information, and to generate key signal input related to the user settings and function control of the mobile terminal. Optionally, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 1071 or near the touch panel 1071 using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a pre-set program. The touch panel 1071 may include two parts: a touch detection device and a touch controller. Optionally, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 110, and can receive and execute the command sent by the processor 110. In addition, the touch panel 1071 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may further include other input devices 1072. Optionally, the other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, a function key (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, etc., which are not specifically limited here. Optionally, the touch panel 1071 may cover the display panel 1061. When the touch panel 1071 detects a touch operation on or near it, it is transmitted 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 in FIG. 1 , the touch panel 1071 and the display panel 1061 are used as two independent components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 can be integrated to implement the input and output functions of the mobile terminal, which is not limited to the specifics herein. The interface unit 108 serves as an interface through which at least one external device can be 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 with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from an external device 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 an external device. The memory 109 can be used to store software programs and various data. The memory 109 can mainly include a program storage area and a data storage area. Optionally, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 109 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. The processor 110 is the control center of the mobile terminal. It uses various interfaces and lines to connect various parts of the entire mobile terminal. It executes various functions of the mobile terminal and processes data by running or executing software programs and / or modules stored in the memory 109, and calling data stored in the memory 109, so as to monitor the mobile terminal as a whole. The processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor and a modem processor. Optionally, the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 110. The mobile terminal 100 may also include a power supply 111 (such as a battery) for supplying power to various components. Preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Although not shown in FIG. 1 , the mobile terminal 100 may further include a Bluetooth module, etc., which will not be described in detail herein. In order to facilitate understanding of the embodiments of the present application, the communication network system on which the mobile terminal of the present application is based is described below. Please refer to Figure 2, which is a communication network system architecture diagram provided in an embodiment of the present application. The communication network system is an LTE system of universal mobile communication technology. The LTE system includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203 and an operator's IP service 204. Optionally, UE201 may be the above-mentioned terminal 100, which will not be described in detail here. E-UTRAN 202 includes eNodeB 2021 and other eNodeBs 2022 , etc. Optionally, eNodeB 2021 may be connected to other eNodeBs 2022 via a backhaul (eg, an X2 interface), and eNodeB 2021 is connected to EPC 203 , and eNodeB 2021 may provide UE 201 with access to EPC 203 . EPC203 may include MME (Mobility Management Entity) 2031, HSS (Home Subscriber Server) 2032, other MMEs 2033, SGW (Serving Gate Way) 2034, PGW (PDN Gate Way) 2035 and PCRF (Policy and Charging Rules Function) 2036, etc. Optionally, MME2031 is a control node that processes signaling between UE201 and EPC203, and provides bearer and connection management. HSS2032 is used to provide some registers to manage functions such as home location register (not shown in the figure), and save some user-specific information such as service features and data rates. All user data can be sent through SGW2034, PGW2035 can provide IP address allocation and other functions for UE 201, PCRF2036 is the policy and charging control policy decision point for service data flow and IP bearer resources, which selects and provides available policy and charging control decisions for the policy and charging execution function unit (not shown in the figure). The IP service 204 may include the Internet, an intranet, an IMS (IP Multimedia Subsystem) or other IP services. Although the above introduction takes the LTE system as an example, those skilled in the art should know that the present application is not only applicable to the LTE system, but also to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, 5G and future new network systems (such as 6G), etc., without limitation here. Based on the above-mentioned mobile terminal hardware structure and communication network system, various embodiments of the present application are proposed. First embodiment 3 is a flow chart of a processing method according to a first embodiment. The processing method of the embodiment of the present application can be applied to a processing device (such as a mobile phone or a server), and includes the following steps: S10: Determine or obtain a candidate prediction mode list according to the prediction mode; S20: Predict the current block according to the candidate prediction mode list to determine or obtain a predicted block. In this embodiment, the processing device may be a smart terminal, such as a mobile phone, a computer, etc., or a server, such as a local server or a cloud server. In this embodiment and this application, the processing device is mainly described as a smart terminal. The current block may be a block being encoded at the current moment, and is hereinafter referred to as an encoding block. Optionally, the encoding block may be a frame of image, or may be an image block obtained by cutting a frame of image, which is not limited here. Optionally, the intelligent terminal can store various images and videos in advance, and can select a frame of image to be predicted from various images as the current block, or cut the selected image and use the cut image block as the current block. Alternatively, a frame of image can be extracted from the video sequence of the video as the current block, or the extracted frame of image can be cut and the obtained image block can be used as the current block. Alternatively, the processing device receives an input image or video, and extracts a frame of image from the image or video as the current block, or performs a segmentation process on the extracted frame of image, and obtains an image block as the current block. Alternatively, the processing device receives an image or video sent by other network devices, and extracts a frame of image from the image or video as the current block, or cuts and processes the extracted frame of image, and the obtained image block is used as the current block. At this time, the processing device establishes a communication connection with the network device on the network side of the mobile communication system in advance, so that the network device can send the image or video to the terminal device through the communication connection, and the terminal device receives the image or video. Optionally, the prediction of the current block includes the prediction of the chroma component (i.e., the chrominance component), the prediction of the luma component (i.e., the brightness component), and the cross-component prediction, etc. For example, for the prediction of the chroma component, the current chroma signal can be predicted by using one or more of the luma component, another chroma component, and the current chroma neighbor information, a mathematical prediction model with parameters is constructed, and the optimization parameters of the mathematical model are calculated. CCLM (Cross-component linear model), FLM (Filter-based Linear Model), and GLM (Filter-based Linear Model) are all linear prediction models. CCCM (convolutional cross-component model), WCP (Worst-Case Perturbations), CCLM_NLT, and GL-CCCM all introduce second-order or nonlinear factor terms, and can have a higher theoretical upper limit than linear models. For example, for chroma prediction, the prediction modes may include CCLM, FLM, GLM, CCCM, GL-CCCM, WCP, etc. The prediction modes of neighboring blocks are included in the candidate list, and all mode indexes in this list are represented by fewer bits, thereby saving bits used to represent the mode and achieving higher coding efficiency. The higher the probability of MPM (Most Probable Modes) hitting, the higher the coding efficiency. The current method of creating / updating the MPM list is to obtain prediction mode information of all neighboring blocks, and then determine or generate the final MPM list based on simple sorting or merging, etc. The current prediction mode determination or generation method still has a large room for improvement in its hit rate to further improve coding efficiency. Optionally, due to the large number of cross-component chroma prediction modes, the bit cost for representing the mode is very high, which in turn limits the further improvement of the encoding and decoding performance. For example: if there are 32 chroma prediction modes, the average representation cost of each mode is 5 bits. Therefore, in this embodiment, a multi-level mode candidate list (that is, a multi-level candidate prediction mode list) can be established for the cross-component chroma prediction of each coding block (such as the current block). And at least one of the tools such as pattern matching, preset calculation rules, neural networks, and query tables is used to dynamically establish a candidate prediction mode list for each coding block, and the modes in the candidate prediction mode list are represented by shorter bit strings. A multi-level candidate prediction mode list is established. When the selected mode is not in the first level, it is determined whether it is in the second level, and so on and so forth to determine the corresponding mode, and prediction is performed according to the determined mode. Optionally, the mode bit string of the first level is the shortest, the second level is the second, and so on and so forth, and the mode bit string outside the mode list is the longest. Optionally, by constructing a candidate prediction mode list, the probability of using the mode list is increased, thereby reducing the mode representation cost and improving compression efficiency. For example: assuming that there are 32 prediction modes for chroma and the mode list has 4 elements, there are 28 modes that are not in the list. The average representation cost of each mode in the list is 2 bits, 40% are non-list modes, and the average representation cost of the mode calculated according to the entropy coding principle is 0.6*2+0.4*log2(28)=3.12294196882. When the hit probability of the list is low, for example 20% (20% are 4 modes in the list, 80% are non-list modes), the average representation cost of the mode calculated according to the entropy coding principle is 0.2*2+0.8*log2(28)=4.24588393765. Optionally, the candidate prediction mode list may be a list, a table, an array, or data in a preset format, which is not limited here. Optionally, step S10 includes: determining or obtaining a candidate prediction mode list according to the prediction mode; Optionally, after the intelligent terminal obtains the prediction mode, it can predict the current block according to the prediction mode to obtain a prediction block, and then perform encoding based on the prediction block. Optionally, before predicting the current block according to the prediction mode, a candidate prediction mode list may be determined or obtained according to the prediction mode, such as creating or updating the candidate prediction mode list according to the prediction mode. Optionally, the prediction mode may be various modes for predicting the coding block, such as a prediction mode of the same classification, a prediction mode across components. Optionally, the prediction mode may be CCLM, FLM, GLM, CCCM, GL-CCCM, WCP, etc. Optionally, the prediction modes may be classified into types, such as being classified into at least one of a neighbor block prediction mode, a non-neighbor block prediction mode, a default prediction mode, and a time domain prediction mode. Optionally, the neighbor block prediction mode includes an adjacent block prediction mode and a non-adjacent block prediction mode. Optionally, the non-neighbor block prediction mode includes a non-adjacent block prediction mode. Optionally, the neighbor block prediction mode may be a prediction mode corresponding to a predicted coding block adjacent to the current block, or a prediction mode of a block adjacent to the current block that has been encoded and decoded, such as a prediction mode adopted by a left adjacent block of the current block. Optionally, the predicted coding block may be a coding block belonging to the same component as the current block, such as belonging to the same chrominance or the same luminance. It may also be a cross-component coding block, such as the current block is a block of chrominance components, and the predicted coding block may be a block of luminance components. Optionally, the non-neighbor block prediction may be a prediction mode corresponding to a predicted coding block that is not adjacent to the current block, or may refer to a prediction mode of a block that has been coded and decoded but is far from the current block. Optionally, the neighboring block prediction mode may be a prediction mode corresponding to a coding block that has been predicted and is in direct contact with the current block. Optionally, the non-adjacent block prediction mode may be a prediction mode corresponding to an already predicted coding block that has no direct contact with the current block. Optionally, the time domain prediction mode may be a prediction mode in the time domain, that is, a prediction mode different from the current frame / time, such as an adjacent block prediction mode and / or a non-adjacent block prediction mode of a co-located block of the current block in a reference frame or collocated picture. Optionally, the time domain prediction mode may also be derived from the prediction mode of the neighboring frame of the frame where the current block is located, and the neighboring frame may be an intra-frame coded frame or an intra-frame inter-frame mixed coded frame. If the frame where the current block is located is a P frame (unidirectional prediction frame) or a B frame (bidirectional prediction frame), let the GOP (Group of picture) number be n, and the neighboring frame GOP number may be n-1, n-2, ..., n+1, n+2, etc., and may be an I frame (key frame, intra-frame coded frame), a P frame, or a B frame. Optionally, the temporal prediction mode is an intra-frame prediction mode, such as a cross-component intra-frame prediction mode of a co-located block in a collocated frame. For the current block, there may be multiple temporal prediction modes, and they may be sorted in a certain order. For example, when a co-located block in a collocated frame adopts an inter-frame prediction mode, the cross-component intra-frame prediction mode of a neighboring block of the co-located block is used instead. Optionally, if the current block is offset from the co-located block in the collocated frame, the offset target block (i.e. the offset co-located block) can be obtained based on the corresponding motion vector, and the cross-component intra-frame prediction mode of the target block can be used as the time domain prediction mode of the current block. Optionally, the default prediction module may be one or more prediction modes that are frequently or highly likely to be used, such as one or more modes with the highest probability of appearing in the coding modes used by all blocks. For example, the LM_CHROMA mode in CCLM is set as the default prediction mode. For example, as shown in FIG4 , the relationship between the current block and its neighbor blocks and non-neighbor blocks, and the relationship between the current block and its adjacent blocks and non-adjacent blocks are exemplified by the encoding end. If the current block to be encoded is Pred, its neighbor blocks include TL1, T1, L1, LB1, TL3, T2, TR2, TL2, TR1, L2, and LB2, etc. Non-neighbor blocks may include B1, B2, B3, B4, B5, and B6. The adjacent blocks and non-adjacent blocks of the current block may be determined according to whether they are adjacent or not. For example, TL1, T1, L1, and LB1 are adjacent blocks of Pred, and the rest are non-adjacent blocks. Optionally, step S10 includes methods 1 to 12, and at least one of method 1: Method 1: creating or updating a candidate prediction mode list according to at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode or a time domain prediction mode; Optionally, the default prediction mode may be one or more, the neighbor block prediction mode may be one or more, the non-neighbor block prediction mode may be one or more, and the time domain prediction mode may be one or more. Optionally, when the prediction mode includes at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode or a time domain prediction mode, a mode list creation or update module can be set, and at least one of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and / or the time domain prediction mode is input into the mode list creation or update module, and a created or updated candidate prediction mode list is output. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, the candidate prediction mode list may include at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode and a time domain prediction mode. Optionally, when updating the candidate prediction mode list, you can first obtain a constructed mode list, and then you can update the mode list through at least one of the default prediction mode, neighbor block prediction mode, non-neighbor block prediction mode and / or time domain prediction mode to obtain an updated candidate prediction mode list. Optionally, a candidate prediction mode list is created or updated according to at least one of a default prediction mode, a neighbor block prediction mode, a cross-component prediction mode of a neighbor block, a non-neighbor block prediction mode, a cross-component prediction mode of a non-neighbor block, and a time domain prediction mode. Optionally, it is only necessary to determine at least one of the default prediction mode, neighbor block prediction mode, non-neighbor block prediction mode, and time domain prediction mode to create or update the candidate prediction mode list, thereby achieving rapid creation or update of the candidate prediction mode list, improving prediction efficiency, and further improving encoding and decoding efficiency. Method 2: determining or obtaining a candidate prediction mode list according to the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block; Optionally, the candidate prediction mode list may include a cross-component candidate prediction mode list for the current block. Optionally, the cross-component prediction mode of the neighboring block may include a prediction mode for a block that is in the same position as the neighboring block but has a different component. For example, if the neighboring block and the current block are both the same chrominance component block and both belong to the U component block, the cross-component prediction mode of the neighboring block may be a prediction mode for a V component block corresponding to the neighboring block, or a prediction mode for a Y component block corresponding to the neighboring block. The cross-component prediction mode of a non-neighboring block may include a prediction mode for a block that is in the same position as the non-neighboring block but has a different component. For example, if the non-neighboring block is a block of the U component, the cross-component prediction mode of the non-neighboring block includes a prediction mode for a block of the V component corresponding to the non-neighboring block, or a prediction mode for a block of the Y component corresponding to the non-neighboring block. Optionally, a candidate prediction mode list is determined or obtained according to at least one of a prediction mode of a chroma component of a neighboring block, a cross-component prediction mode of a neighboring block, a prediction mode of a non-neighboring block, and a cross-component prediction mode of a non-neighboring block. For example, a candidate prediction mode list of a current chroma component is determined or obtained according to a cross-component prediction mode of a neighboring block and a non-neighboring block prediction mode. For example, a candidate prediction mode list of a current chroma component is determined or obtained according to a prediction mode of a chroma component of a neighboring block and a cross-component prediction mode of a non-neighboring block. Optionally, determining or obtaining a candidate prediction mode list according to the prediction mode of the neighbor block and the prediction mode of the non-neighbor block; Optionally, when the current block needs to predict the current chrominance component, a candidate prediction mode list of the current chrominance component may be created or updated according to the prediction mode to determine or obtain the candidate prediction mode list of the current chrominance component. Optionally, the candidate prediction mode list may be determined or obtained based on the prediction mode of a block that has been coded and decoded in the same component as the current block, or may be determined or obtained based on the prediction mode of a block that has been coded and decoded in a different component from the current block. Optionally, determining or obtaining the candidate prediction mode list according to the prediction mode of the neighbor block and the prediction mode of the non-neighbor block may include: determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the neighbor block and the prediction mode of the chrominance component of the non-neighbor block. Alternatively, determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the neighbor block and the prediction mode of the luminance component of the non-neighbor block. Optionally, a candidate prediction mode list for the current chrominance component is determined or obtained according to the inter-component prediction mode of the neighboring block, the inter-component prediction mode of the non-neighboring block, and the time-domain prediction mode. Optionally, if the neighbor block is a block of the U component, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the neighbor block and the prediction mode of the U component of the non-neighboring block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the neighbor block and the prediction mode of the V component of the non-neighboring block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the neighbor block and the prediction mode of the Y component of the non-neighboring block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the neighbor block and the prediction mode of the U component of the non-neighboring block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the neighbor block and the prediction mode of the V component of the non-neighboring block. Optionally, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the neighbor block and the prediction mode of the Y component of the non-neighboring block. Optionally, when determining or obtaining the candidate prediction mode list of the current chroma component, the current chroma component may be the same chroma component as the chroma component of the neighboring block, such as both are U or both are V. The current chroma component may not be the same chroma component as the chroma component of the neighboring block, such as the current chroma component is U and the chroma component of the neighboring block is V, or the current chroma component is V and the chroma component of the neighboring block is U. Optionally, the candidate prediction mode list may include an inter-component prediction mode of a neighbor block and an inter-component prediction mode of a non-neighbor block, and may also include a prediction mode of a neighbor block and a prediction mode of a non-neighbor block. Optionally, a mode list creation or update module may be set up to input at least one of the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block into the mode list creation or update module, and output a candidate prediction mode list. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, a candidate prediction mode list can be determined or obtained based on the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block, so that the cross-component prediction mode can be collected to determine or obtain the candidate prediction mode list, so that the candidate prediction mode list includes cross-component prediction modes instead of just prediction modes of the same component, so that there are more choices when the prediction mode is determined based on the candidate prediction mode list for subsequent prediction, thereby improving the accuracy of the prediction of the current block. Method three: determining or obtaining a candidate prediction mode list according to the cross-component prediction mode of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode; Optionally, the candidate prediction mode list includes a cross-component candidate prediction mode list for the current block. Optionally, the cross-component prediction mode of the neighbor block may include a prediction mode of a block in the same position but different components as the neighbor block. The cross-component prediction mode of the non-neighbor block may include a prediction mode of a block in the same position but different components as the non-neighbor block. Optionally, according to at least one of the prediction mode of the chrominance component of the neighboring block, the cross-component prediction mode of the neighboring block, the time-domain prediction mode, the non-neighboring block prediction mode, and the cross-component prediction mode of the non-neighboring block, the candidate prediction mode list is determined or obtained. For example, according to the cross-component prediction mode of the neighboring block, the non-neighboring block prediction mode and the time-domain prediction mode, the candidate prediction mode list of the current chrominance component is determined or obtained. For example, according to the prediction mode of the chrominance component of the neighboring block, the cross-component prediction mode of the non-neighboring block and the time-domain prediction mode, the candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, determining or obtaining a candidate prediction mode list according to the prediction mode of the neighbor block, the prediction mode of the non-neighbor block and the time domain prediction mode; Optionally, the candidate prediction mode list can be determined or obtained based on the prediction mode of a block that has been encoded and decoded in the same component as the current block and the time domain prediction mode, or the candidate prediction mode list can be determined or obtained based on the prediction mode of a block that has been encoded and decoded in a different component from the current block and the time domain prediction mode. Optionally, determining or obtaining the candidate prediction mode list according to the prediction mode of the neighbor block, the prediction mode of the non-neighbor block, and the time domain prediction mode may include: determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the neighbor block, the prediction mode of the chrominance component of the non-neighbor block, and the time domain prediction mode. Alternatively, determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the neighbor block, the prediction mode of the luminance component of the non-neighbor block, and the time domain prediction mode. Optionally, a candidate prediction mode list for the current chrominance component is determined or obtained according to the inter-component prediction mode of the neighboring block, the inter-component prediction mode of the non-neighboring block, and the time-domain prediction mode. Optionally, if the neighbor block is a block of U component, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the neighbor block, the prediction mode of the U component of the non-neighbor block, and the time domain prediction mode. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the neighbor block, the prediction mode of the V component of the non-neighbor block, and the time domain prediction mode. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the neighbor block, the prediction mode of the Y component of the non-neighbor block, and the time domain prediction mode. Optionally, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the neighbor block, the prediction mode of the U component of the non-neighbor block, and the time domain prediction mode. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the neighbor block, the prediction mode of the V component of the non-neighbor block, and the time domain prediction mode. Optionally, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the neighbor block, the prediction mode of the Y component of the non-neighbor block, and the time domain prediction mode. To the list of candidate prediction modes for the current chroma component. Optionally, when determining or obtaining the candidate prediction mode list of the current chroma component, the current chroma component may be the same chroma component as the chroma component of the neighboring block, such as both are U or both are V. The current chroma component may not be the same chroma component as the chroma component of the neighboring block, such as the current chroma component is U and the chroma component of the neighboring block is V, or the current chroma component is V and the chroma component of the neighboring block is U. Optionally, the candidate prediction mode list may include an inter-component prediction mode of a neighbor block, an inter-component prediction mode of a non-neighbor block, and a time-domain prediction mode, and may also include a prediction mode of a neighbor block and a prediction mode of a non-neighbor block. Optionally, a mode list creation or update module may be set up to input at least one of the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block and the time domain prediction mode into the mode list creation or update module to output a candidate prediction mode list. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, by determining or obtaining a candidate prediction mode list based on the cross-component prediction mode of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time domain prediction mode, it is possible to determine or obtain a candidate prediction mode list that includes not only the cross-component prediction mode but also the time domain prediction mode, thereby providing more choices when subsequently determining the prediction mode based on the candidate prediction mode list for prediction, thereby improving the accuracy of the prediction of the current block. Method 4: Determine or obtain a candidate prediction mode list of the current chrominance component according to the prediction mode of the luminance component of the neighboring block and the prediction mode of the non-neighboring block; Optionally, the current block may be cross-component predicted, and before the cross-component prediction is performed, a candidate prediction mode list of the current chrominance component is constructed. Therefore, according to the prediction mode of the luminance component of the neighboring block and the prediction mode of the non-neighboring block, determining or obtaining the candidate prediction mode list of the current chrominance component may include: According to at least one of the prediction modes of the luminance component of the neighboring block and the prediction modes of the chrominance component of the non-neighboring block, a candidate prediction mode list of the current chrominance component is determined or obtained. Alternatively, according to at least one of the prediction modes of the luminance component of the neighboring block and the prediction modes of the luminance component of the non-neighboring block, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, if the brightness component of the neighboring block is a Y component, a candidate prediction mode list of the current chrominance component may be determined or obtained according to the prediction mode of the Y component of the neighboring block and the prediction mode of the U component of the non-neighboring block. A candidate prediction mode list of the current chrominance component may be determined or obtained according to the prediction mode of the Y component of the neighboring block and the prediction mode of the V component of the non-neighboring block. A candidate prediction mode list of the current chrominance component may be determined or obtained according to the prediction mode of the Y component of the neighboring block and the prediction mode of the Y component of the non-neighboring block. Optionally, a mode list creation or update module may be set up to input at least one of the prediction mode of the brightness component of the neighbor block and the prediction mode of the non-neighbor block into the mode list creation or update module, and output a candidate prediction mode list for the current chrominance component. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, according to the prediction mode of the luminance component of the neighboring block and the cross-component prediction mode of the non-neighboring block, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, according to the prediction mode of the luminance component of the neighboring block, the non-neighboring block prediction mode and the cross-component prediction mode of the non-neighboring block, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, a candidate prediction mode list for the current chrominance component can be determined or obtained based on the prediction mode of the luminance component of the neighboring block and the prediction mode of the non-neighboring block, thereby enabling the collection of cross-component prediction modes to determine or obtain the candidate prediction mode list for the current chrominance component, thereby enabling the candidate prediction mode list to include cross-component prediction modes rather than just prediction modes of the same component, thereby enabling more choices when subsequently determining the prediction mode based on the candidate prediction mode list for prediction, thereby improving the accuracy of the prediction of the current block. Method 5: Determine or obtain a candidate prediction mode of the current chrominance component according to the prediction mode of the luminance component of the neighboring block, the prediction mode of the non-neighboring block and the time domain prediction mode; Optionally, the current block may be cross-component predicted, and before the cross-component prediction is performed, a candidate prediction mode list of the current chrominance component is constructed. Therefore, according to the prediction mode of the luminance component of the neighboring block, the prediction mode of the non-neighboring block and the time domain prediction mode, determining or obtaining the candidate prediction mode list of the current chrominance component may include: According to at least one of the prediction mode of the luminance component of the neighboring block, the prediction mode of the chrominance component of the non-neighboring block, and the time domain prediction mode, determine or obtain the candidate prediction mode list of the current chrominance component. Alternatively, according to at least one of the prediction mode of the luminance component of the neighboring block, the prediction mode of the luminance component of the non-neighboring block, and the time domain prediction mode, determine or obtain the candidate prediction mode list of the current chrominance component. Optionally, if the chrominance component of the neighboring block is a Y component, a candidate prediction mode list of the current chrominance component may be determined or obtained according to the prediction mode of the Y component of the neighboring block, the prediction mode of the U component of the non-neighboring block, and the time domain prediction mode. A candidate prediction mode list of the current chrominance component may be determined or obtained according to the prediction mode of the Y component of the neighboring block, the prediction mode of the V component of the non-neighboring block, and the time domain prediction mode. A candidate prediction mode list of the current chrominance component may be determined or obtained according to the prediction mode of the Y component of the neighboring block, the prediction mode of the Y component of the non-neighboring block, and the time domain prediction mode. Optionally, a mode list creation or update module can be set up to input at least one of the prediction mode of the brightness component of the neighbor block, the non-neighbor block prediction mode and the time domain prediction mode into the mode list creation or update module, and output a candidate prediction mode list for the current chrominance component. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, according to the prediction mode of the brightness component of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, according to the prediction mode of the brightness component of the neighboring block, the non-neighboring block prediction mode, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, by determining or obtaining a candidate prediction mode list for the current chrominance component based on the prediction mode of the luminance component of the neighboring block, the prediction mode of the non-neighboring block, and the time domain prediction mode, it is possible to determine or obtain a candidate prediction mode list that includes not only cross-component prediction modes but also time domain prediction modes, thereby providing more choices when subsequently determining a prediction mode based on the candidate prediction mode list for prediction, thereby improving the accuracy of the prediction of the current block. Method 6: creating or updating a candidate prediction mode list according to a default prediction mode, a time domain prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode; Optionally, when the prediction mode includes a default prediction mode, a time domain prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode, a mode list creation or update module may be set to input at least one of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and the non-adjacent block prediction mode into the mode list creation or update module. Go to the pattern list creation or update module, and output the created or updated candidate prediction pattern list. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, the candidate prediction mode list may include at least one of a default prediction mode, a time domain prediction mode, a neighbor block prediction mode and a non-neighbor block prediction mode. Optionally, a mode list creation or update module may be set up, and the default prediction mode, time domain prediction mode, adjacent block prediction mode and / or non-adjacent block prediction mode may be input into the mode list creation or update module to output a candidate prediction mode list. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, a candidate prediction mode list is created or updated according to at least one of a default prediction mode, a neighbor block prediction mode, a cross-component prediction mode of a neighbor block, a non-neighbor block prediction mode, a cross-component prediction mode of a non-neighbor block, and a time domain prediction mode. Optionally, by creating or updating a candidate prediction mode list according to a default prediction mode, a time domain prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode, it is possible to quickly create or update the candidate prediction mode list, thereby improving prediction efficiency and encoding and decoding efficiency. Method 7: determining or obtaining a candidate prediction mode list according to the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block; Optionally, the candidate prediction mode list may include a cross-component candidate prediction mode list. Optionally, the cross-component prediction mode of an adjacent block may include a prediction mode for a block that is in the same position as the adjacent block but has a different component. For example, if the adjacent block and the current block are both the same chrominance component block and both belong to a U component block, the cross-component prediction mode of the adjacent block may be a prediction mode for a V component block corresponding to the adjacent block, or a prediction mode for a Y component block corresponding to the adjacent block. The cross-component prediction mode of a non-adjacent block may include a prediction mode for a block that is in the same position as the non-adjacent block but has a different component. For example, if the non-adjacent block is a block of the U component, the cross-component prediction mode of the non-adjacent block includes a prediction mode for a block of the V component corresponding to the non-adjacent block, or a prediction mode for a block of the Y component corresponding to the non-adjacent block. Optionally, a candidate prediction mode list is determined or obtained according to at least one of a prediction mode of a chroma component of an adjacent block, a cross-component prediction mode of an adjacent block, a prediction mode of a non-adjacent block, and a cross-component prediction mode of a non-adjacent block. For example, a candidate prediction mode list of a current chroma component is determined or obtained according to a cross-component prediction mode of an adjacent block and a non-adjacent block prediction mode. For example, a candidate prediction mode list of a current chroma component is determined or obtained according to a prediction mode of a chroma component of an adjacent block and a cross-component prediction mode of a non-adjacent block. Optionally, determining or obtaining a candidate prediction mode list according to the prediction mode of the adjacent block and the prediction mode of the non-adjacent block; Optionally, the candidate prediction mode list may be determined or obtained based on the prediction mode of a block that has been coded and decoded in the same component as the current block, or may be determined or obtained based on the prediction mode of a block that has been coded and decoded in a different component from the current block. Optionally, determining or obtaining the candidate prediction mode list according to the prediction mode of the chrominance component of the adjacent block and the prediction mode of the non-adjacent block may include: determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the adjacent block and the prediction mode of the chrominance component of the non-adjacent block. Alternatively, determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the adjacent block and the prediction mode of the luminance component of the non-adjacent block. Optionally, a candidate prediction mode list for the current chroma component is determined or obtained according to the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block. Optionally, if the adjacent block is a block of the U component, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the adjacent block and the prediction mode of the U component of the non-adjacent block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the adjacent block and the prediction mode of the V component of the non-adjacent block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the adjacent block and the prediction mode of the Y component of the non-adjacent block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the adjacent block and the prediction mode of the U component of the non-adjacent block. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the adjacent block and the prediction mode of the V component of the non-adjacent block. Optionally, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the adjacent block and the prediction mode of the Y component of the non-adjacent block. Optionally, when determining or obtaining the candidate prediction mode list of the current chroma component, the current chroma component may be the same chroma component as the chroma component of the adjacent block, such as both are U or both are V. The current chroma component may not be the same chroma component as the chroma component of the adjacent block, such as the current chroma component is U and the chroma component of the adjacent block is V, or the current chroma component is V and the chroma component of the adjacent block is U. Optionally, the candidate prediction modes may include a cross-component prediction mode of an adjacent block and a cross-component prediction mode of a non-adjacent block, and may also include a prediction mode of an adjacent block and a prediction mode of a non-adjacent block. Optionally, a mode list creation or update module may be set up to input at least one of the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block into the mode list creation or update module to output a candidate prediction mode list. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, a candidate prediction mode list can be determined or obtained based on the cross-component prediction modes of adjacent blocks and the cross-component prediction modes of non-adjacent blocks, thereby realizing the collection of cross-component prediction modes to determine or obtain the candidate prediction mode list, and realizing that the candidate prediction mode list includes cross-component prediction modes rather than just prediction modes of the same component, and realizing that when the prediction mode is subsequently determined based on the candidate prediction mode list for prediction, there are more choices, thereby improving the accuracy of the prediction of the current block. Method 8: determining or obtaining a candidate prediction mode list according to the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block and the time-domain prediction mode; Optionally, the candidate prediction mode list includes a cross-component candidate prediction mode list. Optionally, the cross-component prediction mode of the adjacent block may include a prediction mode of a block in the same position but different components as the adjacent block. The cross-component prediction mode of the non-adjacent block may include a prediction mode of a block in the same position but different components as the non-adjacent block. Optionally, according to at least one of the prediction mode of the chrominance component of the adjacent block, the cross-component prediction mode of the adjacent block, the time-domain prediction mode, the non-adjacent block prediction mode and the cross-component prediction mode of the non-adjacent block, the candidate prediction mode list is determined or obtained. For example, according to the cross-component prediction mode of the adjacent block, the non-adjacent block prediction mode and the time-domain prediction mode, the candidate prediction mode list of the current chrominance component is determined or obtained. For example, according to the prediction mode of the chrominance component of the adjacent block, the cross-component prediction mode of the non-adjacent block and the time-domain prediction mode, the candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, the candidate prediction mode list can be determined or obtained based on the prediction mode of a block that has been encoded and decoded in the same component as the current block and the time domain prediction mode, or the candidate prediction mode list can be determined or obtained based on the prediction mode of a block that has been encoded and decoded in a different component from the current block and the time domain prediction mode. Optionally, determining or obtaining the candidate prediction mode list according to the prediction mode of the chrominance component of the adjacent block, the prediction mode of the non-adjacent block, and the time domain prediction mode may include: determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the adjacent block, the prediction mode of the chrominance component of the non-adjacent block, and the time domain prediction mode. Alternatively, determining or obtaining the candidate prediction mode list according to at least one of the prediction mode of the chrominance component of the adjacent block, the prediction mode of the luminance component of the non-adjacent block, and the time domain prediction mode. Optionally, a candidate prediction mode list of the current chrominance component is determined or obtained according to the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block and the time-domain prediction mode. Optionally, if the adjacent block is a block of U component, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the adjacent block, the prediction mode of the U component of the non-adjacent block and the time domain prediction mode. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the adjacent block, the prediction mode of the V component of the non-adjacent block and the time domain prediction mode. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the U component of the adjacent block, the prediction mode of the Y component of the non-adjacent block and the time domain prediction mode. Optionally, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the adjacent block, the prediction mode of the U component of the non-adjacent block and the time domain prediction mode. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the V component of the adjacent block, the prediction mode of the V component of the non-adjacent block and the time domain prediction mode. Optionally, a candidate prediction mode list of the current chrominance component may be determined or obtained according to the prediction mode of the V component of the adjacent block, the prediction mode of the Y component of the non-adjacent block and the time domain prediction mode. Optionally, when determining or obtaining the candidate prediction mode list of the current chroma component, the current chroma component may be the same chroma component as the chroma component of the adjacent block, such as both are U or both are V. The current chroma component may not be the same chroma component as the chroma component of the adjacent block, such as the current chroma component is U and the chroma component of the adjacent block is V, or the current chroma component is V and the chroma component of the adjacent block is U. Optionally, the candidate prediction mode list may include cross-component prediction modes of adjacent blocks, cross-component prediction modes of non-adjacent blocks, and time-domain prediction modes, and may also include prediction modes of adjacent blocks and prediction modes of non-adjacent blocks. Optionally, a mode list creation or update module may be set up to input at least one of the cross-component prediction mode of adjacent blocks, the cross-component prediction mode of non-adjacent blocks and the time domain prediction mode into the mode list creation or update module to output a candidate prediction mode list. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, by determining or obtaining a candidate prediction mode list based on the cross-component prediction mode of adjacent blocks, the cross-component prediction mode of non-adjacent blocks, and the time-domain prediction mode, it is possible to determine or obtain a candidate prediction mode list that includes not only the cross-component prediction mode but also the time-domain prediction mode, thereby providing more choices when subsequently determining the prediction mode based on the candidate prediction mode list for prediction, thereby improving the accuracy of the prediction of the current block. Method 9: Determine or obtain a candidate prediction mode list of the current chrominance component based on the luminance component prediction mode of the adjacent block and the prediction mode of the non-adjacent block; optionally, a cross-component prediction can be performed on the current block, and before the cross-component prediction is performed, a candidate prediction mode list of the current chrominance component is constructed. Therefore, determining or obtaining a candidate prediction mode list of the current chrominance component based on the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block may include: According to the prediction mode of the luminance component of the adjacent block and the prediction mode of the chrominance component of the non-adjacent block, a candidate prediction mode list of the current chrominance component is determined or obtained. Alternatively, according to the prediction mode of the luminance component of the adjacent block and the prediction mode of the luminance component of the non-adjacent block, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, if the chrominance component of the adjacent block is a Y component, a candidate prediction mode list of the current chrominance component may be determined or obtained based on the prediction mode of the Y component of the adjacent block and the prediction mode of the U component of the non-adjacent block. A candidate prediction mode list of the current chrominance component may be determined or obtained based on the prediction mode of the Y component of the adjacent block and the prediction mode of the V component of the non-adjacent block. A candidate prediction mode list of the current chrominance component may be determined or obtained based on the prediction mode of the Y component of the adjacent block and the prediction mode of the Y component of the non-adjacent block. Optionally, a mode list creation or update module may be set up, the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block are input into the mode list creation or update module, and a candidate prediction mode list of the current chrominance component is output. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, according to the prediction mode of the luminance component of the adjacent block and the cross-component prediction mode of the non-adjacent block, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, according to the prediction mode of the luminance component of the adjacent block, the non-adjacent block prediction mode and the cross-component prediction mode of the non-adjacent block, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, a candidate prediction mode list for the current chrominance component can be determined or obtained based on the prediction mode of the luminance component of the adjacent blocks and the prediction mode of the non-adjacent blocks, thereby enabling the collection of cross-component prediction modes to determine or obtain the candidate prediction mode list for the current chrominance component, thereby enabling the candidate prediction mode list to include cross-component prediction modes rather than just prediction modes of the same component, thereby enabling more choices when subsequently determining the prediction mode based on the candidate prediction mode list for prediction, thereby improving the accuracy of the prediction of the current block. Method 10: Determine or obtain a candidate prediction mode list of the current chrominance component according to the luminance component prediction mode, the non-adjacent prediction mode and the time domain prediction mode of the adjacent block; Optionally, the current block may be cross-component predicted, and before the cross-component prediction is performed, a candidate prediction mode list of the current chrominance component is constructed. Therefore, according to the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode, determining or obtaining the candidate prediction mode list of the current chrominance component may include: According to at least one of the prediction mode of the luminance component of the adjacent block, the prediction mode of the chrominance component of the non-adjacent block, and the time domain prediction mode, determine or obtain the candidate prediction mode list of the current chrominance component. Alternatively, according to at least one of the prediction mode of the luminance component of the adjacent block, the prediction mode of the luminance component of the non-adjacent block, and the time domain prediction mode, determine or obtain the candidate prediction mode list of the current chrominance component. Optionally, if the chrominance component of the adjacent block is a Y component, the candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the Y component of the adjacent block, the prediction mode of the U component of the non-adjacent block, and the time domain prediction mode. The candidate prediction mode list of the current chrominance component can be determined or obtained according to the prediction mode of the Y component of the adjacent block, the prediction mode of the Y component of the non-adjacent block and the time domain prediction mode. Optionally, a mode list creation or update module can be set up, and at least one of the prediction mode of the brightness component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode is input into the mode list creation or update module, and a candidate prediction mode list of the current chrominance component is output. Optionally, at least one of a neural network, a pattern matching rule, a mathematical pattern, and a query table can be set in the pattern list creation or update module. Optionally, according to the prediction mode of the luminance component of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, according to the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode, a candidate prediction mode list of the current chrominance component is determined or obtained. Optionally, by determining or obtaining a candidate prediction mode list for the current chrominance component based on the prediction mode of the luminance component of the adjacent blocks, the prediction mode of the non-adjacent blocks, and the time domain prediction mode, it is possible to determine or obtain a candidate prediction mode list that includes not only cross-component prediction modes but also time domain prediction modes, thereby providing more choices when subsequently determining a prediction mode based on the candidate prediction mode list for prediction, thereby improving the accuracy of the prediction of the current block. Method eleven: Determine at least one prediction mode according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixels, and create or update the candidate prediction mode list according to the at least one prediction mode. Optionally, the neighbor block pixels may refer to pixels composed of at least one neighbor block adjacent to the current block, or may refer to pixels composed of at least one adjoining block adjacent to the current block. Optionally, the default prediction mode may include one or more prediction modes. The neighbor block prediction mode may include one or more prediction modes corresponding to the neighbor blocks. The non-neighbor block prediction mode may include one or more prediction modes corresponding to the non-neighbor blocks. Optionally, at least one prediction mode may be selected and determined from the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the neighbor block pixels, and then the candidate prediction mode list is created or updated according to the at least one prediction mode. Optionally, at least one prediction mode is determined according to the default prediction mode, the neighbor block prediction mode, the cross-component prediction mode of the neighbor block, the non-neighbor block prediction mode, the cross-component prediction mode of the non-neighbor block, and the neighbor block pixels. Optionally, an empty prediction mode list may be constructed, and the determined at least one prediction mode may be embedded into the empty prediction mode list to create or update the prediction mode list. Optionally, a pattern list creation or update module may be set up to input at least one determined prediction mode into the pattern list or update module, and output a created candidate prediction mode list, or output an updated candidate prediction mode list. For example, as shown in FIG5 , a pattern list creation / update module is set, and the default prediction mode, neighbor block prediction mode, non-neighbor block prediction mode, neighbor block pixel, first level list, and second level list are input into the pattern list creation / update module, and a new first level list and a new second level list are output. A neural network, pattern matching, mathematical model, or query table can be set in the pattern list creation / update module. Optionally, the first level list and the second level list can be prediction pattern lists of different levels, and the new first level list and the new second level list can be candidate prediction pattern lists. Optionally, if it is necessary to create a candidate prediction mode list, the mode list creation / update module (i.e., mode list creation or update module) can determine or generate the candidate prediction mode list based on all known information using tools such as neural networks, pattern matching, mathematical models, and query tables. Optionally, all known information includes at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, and a neighbor block pixel. If the prediction mode list needs to be updated, the mode list creation / update module (i.e., mode list creation or update module) can determine or generate a candidate prediction mode list based on all known information using tools such as neural networks, pattern matching, mathematical models, and query tables. For example, a prediction mode is inserted into the existing first list to obtain a new first-level list. Optionally, all known information includes at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, neighbor block pixels, and at least one of a first level list and a second level list. Optionally, by determining at least one prediction mode based on the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixels, and then creating or updating the candidate prediction mode list based on the at least one prediction mode, it is possible to select at least one appropriate prediction mode based on the neighbor block pixels, thereby making the candidate prediction mode list created or updated based on the at least one prediction mode more effective. Method 12: Determine at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode and a time domain prediction mode according to neighbor block pixels, and create or update a candidate prediction mode list according to the at least one prediction mode. Optionally, the default prediction mode may be one or more. The neighbor block prediction mode may be at least one prediction mode corresponding to at least one neighbor block. The non-neighbor block prediction mode may be at least one prediction mode corresponding to at least one non-neighbor block. Optionally, the costs corresponding to each default prediction mode, each neighbor block prediction mode, each non-neighbor block prediction mode and each time domain prediction mode may be determined according to neighbor block pixels, and then at least one prediction mode may be selected according to the costs. Optionally, at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a cross-component prediction mode of a neighbor block, a non-neighbor block prediction mode, a cross-component prediction mode of a non-neighbor block and a time domain prediction mode is determined according to neighbor block pixels. Optionally, the SAD value of each prediction mode may be calculated based on the neighboring block pixels, and the corresponding cost may be determined based on the SAD value, for example, the SAD value may be directly used as the corresponding cost. Optionally, an empty prediction mode list can be constructed. After determining at least one prediction mode among the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the time domain prediction mode based on the neighbor block pixels, at least one prediction mode can be written into the empty prediction mode list, and the created candidate prediction mode list can be output, or the updated candidate prediction mode list can be output. Optionally, step S20 includes: predicting the current block according to the candidate prediction mode list to determine or obtain a predicted block. Optionally, when predicting the current block, the Y component may be predicted, the U component may be predicted, or the V component may be predicted. Optionally, the current frame is shown in FIG6, the Y component (i.e., brightness component) corresponding to the current frame is shown in FIG7, the U component (i.e., chrominance component) corresponding to the current frame is shown in FIG8, and the V component (i.e., another chrominance component) corresponding to the current frame is shown in FIG9. The current block may be a block that is not encoded or decoded after the current frame is divided into blocks. Optionally, at least one prediction mode is selected from the candidate prediction mode list to predict the current block, and a predicted block is determined or obtained. Optionally, a candidate prediction mode list corresponding to any block to be encoded, such as the current block, may be established, and then at least one prediction mode in the candidate prediction mode list may be selected to predict the current block to determine or obtain a predicted block. Optionally, the current block may be predicted at the encoding side to determine or obtain the predicted block, or the current block may be predicted at the decoding side to determine or obtain the predicted block. Optionally, by determining at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode and a time domain prediction mode based on neighbor block pixels, and then creating or updating a candidate prediction mode list based on at least one prediction mode, it is possible to select at least one appropriate prediction mode based on neighbor block pixels, thereby making the candidate prediction mode list created or updated based on at least one prediction mode more effective. Method 1: Create or update a candidate prediction mode list according to a time domain prediction mode for obtaining displacement / offset of a motion vector and at least one prediction mode among prediction modes based on historical records. Optionally, the prediction mode of the historical record may be a prediction mode that has been stored and used in advance. Optionally, the prediction mode of the historical record may include at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, and a time domain prediction mode, and may also include a cross-component prediction mode, etc. Optionally, the history-based prediction patterns may be stored in a history-based table. Optionally, a history-based table is maintained to contain the most recently used CCP models and is reset at the beginning of each CTU row. If the current list is not full after including spatially adjacent and non-adjacent candidates, the CCP model in the history-based table is added to the candidate prediction mode list. Optionally, if the current block is displaced / offset with respect to the co-located block in the juxtaposed frame, the offset target block (i.e., the offset co-located block) can be obtained based on the corresponding motion vector, and the cross-component intra-frame prediction mode of the target block can be used as the time domain prediction mode of the current block, that is, the above-mentioned displacement / offset time domain prediction mode. Optionally, an empty prediction mode list may be constructed, and at least one prediction mode among the time domain prediction mode based on motion vector or displacement / offset and the prediction mode based on historical records may be embedded into the empty prediction mode list to create or update the prediction mode list. Optionally, a mode list creation or update module can be set up to input the time domain prediction mode based on the motion vector or displacement / offset and at least one prediction mode based on the historical record into the mode list or update module, and output the created candidate prediction mode list, or output the updated candidate prediction mode list. Optionally, a pattern list creation / update module may be provided, and at least one prediction mode, a first-level list, and a second-level list in the time domain prediction mode based on motion vectors or displacements / offsets, and a prediction mode based on historical records are input into the pattern list creation / update module, and a new first-level list and a new second-level list are output. A neural network, pattern matching, a mathematical model, or a query table may be provided in the pattern list creation / update module. Optionally, the first-level list and the second-level list may be prediction mode lists of different levels, and the new first-level list and the new second-level list may be candidate prediction mode lists. Optionally, if it is necessary to create a candidate prediction mode list, the mode list creation / update module (i.e., the mode list creation or update module) can determine or generate the candidate prediction mode list based on all known information using tools such as neural networks, pattern matching, mathematical models, and query tables. Optionally, all known information includes a time domain prediction mode based on a motion vector or a displacement / offset, and at least one prediction mode in a prediction mode based on historical records. If the prediction mode list needs to be updated, the mode list creation / update module (i.e., mode list creation or update module) can determine or generate a candidate prediction mode list based on all known information using tools such as neural networks, pattern matching, mathematical models, and query tables. For example, a prediction mode is inserted into the existing first list to obtain a new first-level list. Optionally, by obtaining a time domain prediction mode of displacement / offset based on a motion vector and at least one prediction mode among the prediction modes based on historical records, a candidate prediction mode list is created or updated, thereby enabling at least one suitable prediction mode to be selected based on the motion vector or the historical records, thereby making the candidate prediction mode list created or updated based on at least one prediction mode more effective. In this embodiment, by determining or obtaining a candidate prediction mode list according to the prediction mode, the current block is predicted according to the candidate prediction mode list to determine or obtain a prediction block. Through the above technical solution, it is possible to avoid the phenomenon that when the number of prediction modes is large, the prediction mode is not reasonably planned to improve the hit rate, resulting in a high prediction cost. By first determining or obtaining a candidate prediction mode list according to the prediction mode, so that the prediction mode that may be used for the current block is reasonably planned into the candidate prediction mode list, and then predicting the current block through the candidate prediction mode list, determining or obtaining a prediction block, thereby reducing the mode representation cost of the prediction mode for predicting the current block, reducing the prediction cost of the prediction block, and thereby improving the efficiency of video encoding and / or decoding. Second embodiment Based on any of the above embodiments, a second embodiment is proposed. In this embodiment, the eleventh method determines at least one prediction mode according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixel, including at least one of the methods 2 to 6: Mode 2, calculating the SAD values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the neighbor block pixels, and determining at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the SAD values; Optionally, referring to FIG. 10 , method 2 may include step a and step b. Step a, calculating the SAD values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the neighbor block pixels; In this embodiment, on the decoding side, when calculating the SAD values ​​of the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode, the SAD calculation of the neighbor block can be used instead. On the encoding side, the current block can be directly used to calculate the SAD values ​​of the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode. Optionally, SAD can be the sum of the absolute values ​​of the differences between the corresponding values ​​of each pixel, based on which the similarity between two image blocks is judged. Therefore, when calculating the SAD value corresponding to each prediction mode, the pixel values ​​of the neighboring blocks or non-neighboring blocks using the prediction mode before and after prediction can be compared based on the neighboring block pixels to determine the SAD value corresponding to the prediction mode. Optionally, when calculating the SAD of the current block using various prediction modes, SAD=Sum|Org-Pred| can be used for calculation, where Pred is the prediction block, Org is the original block, and Sum|| is the sum of the absolute values ​​of all values, such as first determining the absolute value of the difference between the pixel values ​​at the same position in the original block and the prediction block, and then calculating the sum of the absolute values ​​corresponding to all positions of the original block and the prediction block. Optionally, at the decoding side, the SAD value of the left block and / or the upper neighboring pixel block of the current block in the prediction mode is used for approximate calculation to obtain the SAD value of the current block. Step b, determining at least one of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the SAD value. Test mode. Optionally, after determining the SAD value corresponding to the default prediction mode, the SAD value corresponding to the neighbor block prediction mode, and the SAD value corresponding to the non-neighbor block prediction mode, a prediction mode can be selected based on each SAD value, for example, a prediction mode corresponding to a SAD value less than a preset SAD threshold is selected among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode. Optionally, a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode are selected, and the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode are sorted according to respective SAD values ​​to obtain a candidate prediction mode list. For example, as shown in FIG11 , the prediction mode is obtained from the encoded and decoded block. The prediction modes of the adjacent blocks are MMLM_CHROMA (CCCM) and MDLM_L (CCLM), and the prediction modes of the non-adjacent blocks are MMLM_CHROMA (CCLM) and MDLM_T (GL-CCCM). Optionally, some blocks may not use the cross-component prediction mode, and different blocks may use exactly the same prediction mode. For example, if the prediction mode used by the current block is CCLM, the prediction mode used by the neighboring block may be CCLM or PLANAR. PLANAR is a conventional smooth prediction. As shown in FIG. 11 , the LM_CHROMA (CCLM) mode is the default mode, and for the current block Pred, a total of 5 candidate modes are obtained, and the candidate modes are also prediction modes. Optionally, the SAD values ​​(i.e., the sum of absolute errors) of the five prediction modes can be calculated based on the neighboring block pixels, namely: LM_CHROMA (CCLM) = 23043, MMLM_CHROMA (CCCM) = 132454, MDLM_L (CCLM) = 13232, MMLM_CHROMA (CCLM) = 433433, MDLM_T (GL-CCCM) = 54545. Then, the five sums of absolute errors are sorted from small to large to obtain a sorted candidate prediction mode list, namely: MDLM_L (CCLM), LM_CHROMA (CCLM), MDLM_T (GL-CCCM), MMLM_CHROMA (CCCM), MMLM_CHROMA (CCLM). Optionally, the prediction mode with a higher ranking will be represented by using fewer bits to improve the coding rate. Optionally, at least one prediction mode is determined according to the default prediction mode, the adjacent block prediction mode, the non-adjacent block prediction mode and the neighboring block pixels. Optionally, the SAD values ​​of the default prediction mode, the adjacent block prediction mode and the non-adjacent block prediction mode are calculated according to the neighboring block pixels, and at least one prediction mode among the default prediction mode, the adjacent block prediction mode and the non-adjacent block prediction mode is determined according to the SAD values. The specific implementation method can refer to the above steps a and b, which will not be repeated here. In this embodiment, in mode 12, at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode and a time domain prediction mode is determined according to the neighbor block pixels, and a candidate prediction mode list is created or updated according to the at least one prediction mode, including: The SAD values ​​of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the time domain prediction mode are calculated according to the neighbor block pixels, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the time domain prediction mode is determined according to the SAD value; and according to at least one prediction mode, a candidate prediction mode list is created or updated. The specific implementation method can refer to the above steps a and b, which will not be repeated here. Optionally, at least one prediction mode among a default prediction mode, an adjacent block prediction mode, a non-adjacent block prediction mode and a time domain prediction mode is determined according to neighboring block pixels, and a candidate prediction mode list is created or updated according to the at least one prediction mode. Optionally, SAD values ​​of a default prediction mode, an adjacent block prediction mode, a non-adjacent block prediction mode, and a time domain prediction mode are calculated based on neighbor block pixels; and at least one prediction mode among the default prediction mode, the adjacent block prediction mode, the non-adjacent block prediction mode, and the time domain prediction mode is determined based on the SAD values. Optionally, at least one prediction mode in the default prediction mode may be determined based on the neighboring block pixels, or at least one prediction mode in the neighboring block prediction mode may be determined based on the neighboring block pixels, or at least one prediction mode in the non-neighboring block prediction mode may be determined based on the neighboring block pixels, or at least one prediction mode in the time domain prediction mode may be determined based on the neighboring block pixels, or at least one prediction mode in the adjacent block prediction mode may be determined based on the neighboring block pixels, or at least one prediction mode in the non-adjacent block prediction mode may be determined based on the neighboring block pixels. Optionally, the method of determining at least one prediction mode based on the neighboring block pixels may be to calculate the SAD value of each prediction mode based on the neighboring block pixels, and select at least one prediction mode based on the SAD value. Optionally, for mode 1, a candidate prediction mode list is created or updated according to a time domain prediction mode for obtaining displacement / offset of a motion vector and at least one prediction mode of a prediction mode based on a historical record, including: Optionally, based on a reconstructed area of ​​a reference frame of the frame in which the current block pointed to by the motion vector is located, and based on the pixels in the reconstructed area, the SAD value of the time domain prediction mode of the displacement / offset and the SAD value of the prediction mode based on the historical record are calculated, and at least one of the time domain prediction mode of the displacement / offset and the prediction mode based on the historical record is determined based on the SAD value. In this embodiment, by calculating the SAD values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the neighbor block pixels, and then determining at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the SAD value, the effectiveness of the determined prediction mode can be guaranteed, so as to select a suitable prediction mode to construct a candidate prediction mode list. Mode 3, calculating cost values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode according to neighbor block pixels, and determining at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the cost values; Optionally, when calculating the cost value, it can be calculated according to different cost functions, such as SAD (Sum of Absolute Differences), SATD (Sum of Transformed Absolute Differences), MRSAD (Mean Removal SAD), MRSATD (Mean-Removed Sum of Absolute Transformed Differences), etc. Optionally, in the present embodiment, when calculating the cost value of at least one prediction mode, the pixels used are all reconstructed pixels. That is, the neighbor block pixels can be reconstructed pixels. Optionally, the cost value calculation for any prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode can be calculated in the same way, for example, when the neighbor block pixel adopts the neighbor block prediction mode, the prediction value and the reconstruction value of the neighbor block pixel are calculated, and the prediction value and the reconstruction value of the neighbor block pixel are input into the cost function for calculation to obtain the cost value of the neighbor block prediction mode. When the neighbor block pixel adopts the default prediction mode, the prediction value and the reconstruction value of the neighbor block pixel are calculated, and the prediction value and the reconstruction value of the neighbor block pixel are input into the cost function for calculation to obtain the cost value of the default prediction mode. When the neighbor block pixel adopts the non-neighbor block prediction mode, the prediction value and the reconstruction value of the neighbor block pixel are calculated, and the prediction value and the reconstruction value of the neighbor block pixel are input into the cost function for calculation to obtain the cost value of the non-neighbor block prediction mode. Optionally, after determining the cost value of at least one prediction mode, the prediction modes may be arranged from small to large according to the cost value, and at least one prediction mode may be selected from the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the arranged cost value, and then the candidate prediction mode list may be created or updated according to the selected at least one prediction mode. Optionally, for method one, it can include calculating the cost of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode or the time domain prediction mode, determining at least one of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode or the time domain prediction mode based on the cost value, and creating or updating a candidate prediction mode list based on at least one of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode or the time domain prediction mode. Optionally, for the second method, it may include calculating a cost value of a cross-component prediction mode of a neighboring block and a cost value of a cross-component prediction mode of a non-neighboring block. Determine at least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block. Optionally, for the third method, it may include calculating the cost value of the cross-component prediction mode of the neighboring block, the cost value of the cross-component prediction mode of the non-neighboring block, and the cost value of the time-domain prediction mode. Determine at least one of the cross-component prediction mode of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of the cross-component prediction mode of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode. Optionally, for method four, it can include calculating the cost value of the prediction mode of the brightness component of the neighboring block and the prediction mode of the non-neighboring block, determining at least one of the prediction mode of the brightness component of the neighboring block and the prediction mode of the non-neighboring block according to the cost value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to at least one of the prediction mode of the brightness component of the neighboring block and the prediction mode of the non-neighboring block. Optionally, for method five, it can include calculating the cost values ​​of the prediction mode of the brightness component of the neighboring block, the prediction mode of the non-neighboring block and the time domain prediction mode, determining at least one of the prediction mode of the brightness component of the neighboring block, the prediction mode of the non-neighboring block and the time domain prediction mode according to the cost value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to at least one of the prediction mode of the brightness component of the neighboring block, the prediction mode of the non-neighboring block and the time domain prediction mode. Optionally, for method six, it can include calculating the cost values ​​of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and the non-adjacent block prediction mode, determining at least one of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode according to the cost values, and creating or updating a candidate prediction mode list according to at least one of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode. Optionally, for method seven, it may include calculating a cost value of a cross-component prediction mode of an adjacent block and a cost value of a cross-component prediction mode of a non-adjacent block. Determine at least one of the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block. Optionally, for mode eight, it may include calculating a cost value of a cross-component prediction mode of an adjacent block, a cost value of a cross-component prediction mode of a non-adjacent block, and a cost value of a time-domain prediction mode. Determine at least one of the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode. Optionally, for method nine, it can include calculating the cost value of the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block, determining at least one of the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block according to the cost value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to the prediction mode of the luminance component of the adjacent block and at least one of the prediction modes of the non-adjacent block. Optionally, for method ten, it can include calculating the cost values ​​of the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode, determining at least one of the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode according to the cost value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to at least one of the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode. Optionally, for method twelve, it can include calculating cost values ​​of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the time domain prediction mode based on the neighbor block pixels, determining at least one prediction mode among the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the time domain prediction mode based on the cost values, and creating or updating a candidate prediction mode list based on at least one prediction mode. Optionally, for method 1, it can include obtaining a time domain prediction mode of displacement / offset based on a motion vector, determining a prediction mode of a historical record, calculating a cost value of the time domain prediction mode of the displacement / offset, and calculating the cost value of the prediction mode of the historical record, determining at least one prediction mode among the time domain prediction mode of the displacement / offset and the prediction mode of the historical record based on the cost value, and creating or a functional candidate prediction mode list based on at least one prediction mode. In this embodiment, by calculating the cost values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the neighbor block pixels, and then determining at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the cost values, the effectiveness of the determined prediction mode can be guaranteed, so as to select a suitable prediction mode to construct a candidate prediction mode list. Mode 4, when the cost mark obtained in the bitstream is a first value, first generation values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode are calculated according to neighbor block pixels, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode is determined according to the first generation value; Optionally, when the processing device is a decoding end, a cost mark can be obtained in the bitstream, and when the cost mark is determined to be a first value, a target cost function corresponding to the first value can be determined. Optionally, different cost functions can be set in advance to correspond to different cost mark values. For example, when the cost mark value is a first value, the cost function can be SATD. When the cost mark value is a second value, the cost function can be SAD. Or, when the cost mark value is a first value, the cost function can be SAD. When the cost mark value is a second value, the cost function can be SATD. Optionally, the first value can be any value set in advance by the user, such as 0. Optionally, when calculating the first generation values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode based on the neighbor block pixels, the first generation value can be calculated by referring to the calculation method in the above-mentioned method fifteen, that is, the same method as the method fifteen for calculating the cost values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode based on the neighbor block pixels is adopted. Optionally, after determining the first generation value of the default prediction mode, the first generation value of the neighbor block prediction mode, and the first generation value of the non-neighbor block prediction mode, the first generation values ​​may be arranged from small to large according to their sizes, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode may be selected and determined according to the arranged first generation values. For example, the prediction mode corresponding to the smallest first generation value (such as the default prediction mode) may be selected. Optionally, a candidate prediction mode list may be created or updated according to at least one selected prediction mode. Optionally, for method one, it may include calculating a first generation value of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode when the cost mark obtained in the bitstream is a first value, determining at least one of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode, or the time domain prediction mode based on the first generation value, and creating or updating a candidate prediction mode list based on at least one of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode, or the time domain prediction mode. Optionally, for the second method, when the cost mark obtained in the bitstream is the first value, the first generation value of the cross-component prediction mode of the neighboring block and the first generation value of the cross-component prediction mode of the non-neighboring block are calculated. At least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block is determined according to the first generation value, and a candidate prediction mode list is determined or obtained according to at least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block. Optionally, for the third method, when the cost mark obtained in the bitstream is the first value, the first generation value of the cross-component prediction mode of the neighbor block, the first generation value of the cross-component prediction mode of the non-neighbor block, and the first generation value of the time-domain prediction mode can be calculated. At least one of the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode is determined according to the first generation value, and a candidate prediction mode list is determined or obtained according to at least one of the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode. Optionally, for method four, it can include calculating the first generation value of the prediction mode of the luminance component of the neighboring block and the prediction mode of the non-neighboring block when the cost mark obtained in the code stream is the first value, determining at least one of the prediction mode of the luminance component of the neighboring block and the prediction mode of the non-neighboring block according to the first generation value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to at least one of the prediction mode of the luminance component of the neighboring block and the prediction mode of the non-neighboring block. Optionally, for method five, it can include calculating the first generation value of the prediction mode of the brightness component of the neighbor block, the prediction mode of the non-neighbor block and the time domain prediction mode when the cost obtained in the code stream is marked as the first value, determining at least one of the prediction mode of the brightness component of the neighbor block, the prediction mode of the non-neighbor block and the time domain prediction mode according to the first generation value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to at least one of the prediction mode of the brightness component of the neighbor block, the prediction mode of the non-neighbor block and the time domain prediction mode. Optionally, for method six, it can include calculating the first generation values ​​of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and the non-adjacent block prediction mode when the cost mark obtained in the code stream is the first value, determining at least one of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode according to the first generation value, and creating or updating the candidate prediction mode list according to at least one of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode. Optionally, for method seven, when the cost mark obtained in the bitstream is the first value, the first generation value of the cross-component prediction mode of the adjacent block and the first generation value of the cross-component prediction mode of the non-adjacent block are calculated. At least one of the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block is determined according to the first generation value, and a candidate prediction mode list is determined or obtained according to at least one of the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block. Optionally, for mode eight, when the cost mark obtained in the bitstream is the first value, the first generation value of the cross-component prediction mode of the adjacent block, the first generation value of the cross-component prediction mode of the non-adjacent block, and the first generation value of the time-domain prediction mode are calculated. At least one of the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode is determined according to the first generation value, and a candidate prediction mode list is determined or obtained according to at least one of the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode. Optionally, for method nine, it can include calculating the first generation value of the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block when the cost mark obtained in the code stream is the first value, determining at least one of the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block according to the first generation value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to the prediction mode of the luminance component of the adjacent block and at least one of the prediction modes of the non-adjacent block. Optionally, for method ten, it can include calculating the first generation value of the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode when the cost mark obtained in the code stream is the first value, determining at least one of the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode according to the first generation value, and determining or obtaining a candidate prediction mode list for the current chrominance component according to at least one of the prediction mode of the luminance component of the adjacent block, the prediction mode of the non-adjacent block and the time domain prediction mode. Optionally, for method twelve, it can include calculating the first generation values ​​of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the time domain prediction mode according to the neighbor block pixels when the cost mark obtained in the code stream is the first value, determining at least one prediction mode among the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the time domain prediction mode according to the first generation value, and creating or updating a candidate prediction mode list according to at least one prediction mode. Optionally, for method 1, it can include obtaining a time domain prediction mode of displacement / offset according to a motion vector when the cost mark obtained in the bitstream is a first value, determining a prediction mode of a historical record, calculating a first generation value of the time domain prediction mode of the displacement / offset, and calculating a first generation value of the prediction mode of the historical record, determining at least one prediction mode of the time domain prediction mode of the displacement / offset and the prediction mode of the historical record according to the first generation value, and creating or a functional candidate prediction mode list according to at least one prediction mode. In this embodiment, when the processing device is a decoding end and the cost mark obtained in the code stream is a first value, the first generation values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode are calculated according to the neighbor block pixels, and then at least one prediction mode of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode is determined according to the first generation value, thereby ensuring the effectiveness of the determined prediction mode, so as to select a suitable prediction mode to construct a candidate prediction mode list. Mode 5, obtaining a cost tag in a bitstream, selecting a first cost calculation mode when the cost tag is a first value, and selecting a second cost calculation mode when the cost tag is a second value, calculating a cost value of pixels of a neighboring block according to the selected first cost calculation mode or the second cost calculation mode, and determining at least one prediction mode among the default prediction mode, the neighboring block prediction mode, and the non-neighboring block prediction mode according to the cost value; Optionally, when the processing device is a decoding end, the cost mark in the code stream can be obtained, and when the cost mark is a first value, the first cost calculation method can be selected, and when the cost mark is a second value, the second cost calculation method can be selected. The selection method can be selected in a mapping table set in advance, and the mapping table is provided with a correspondence between different cost calculation methods and different cost mark values. Optionally, when there are multiple cost calculation methods using different cost functions for cost value calculation, the cost functions used by different cost calculation methods are different. For example, the first cost calculation method can use SATD for cost value calculation. The second cost calculation method can use SAD for calculation. Optionally, the first value is not equal to the second value, for example, the first value is 0 and the second value is 1. Optionally, a cost calculation method can be composed of the calculation of a single or multiple cost functions, such as SAD+SATD can be a cost calculation method, and SAD*SAD can also be a cost calculation method. Optionally, when the cost mark obtained in the bitstream is a first value, a selected first cost calculation method is determined, a target cost function corresponding to the first cost calculation method is determined, and the neighbor block pixels are input into the target cost function for calculation to obtain a cost value. The cost value may include first cost values ​​corresponding to a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode, respectively. Optionally, when the cost mark obtained in the bitstream is a second value, determining the selected second cost calculation method, determining the target value corresponding to the second cost calculation method A target cost function is provided, and the neighbor block pixels are input into the target cost function for calculation to obtain a cost value, which may include second generation values ​​corresponding to the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode, respectively. Optionally, when calculating the cost value according to the neighbor block pixels, the second cost value may be calculated by referring to the calculation method in the above-mentioned method 15, that is, the cost values ​​of the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode are calculated according to the neighbor block pixels in the same manner as in method 3. No repeated explanation is given here. Optionally, after determining the first generation value of the default prediction mode, the first generation value of the neighbor block prediction mode, and the first generation value of the non-neighbor block prediction mode, the first generation values ​​may be arranged from small to large according to their sizes, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode may be selected and determined according to the arranged first generation values. For example, the prediction mode corresponding to the smallest first generation value (such as the default prediction mode) may be selected. Optionally, a candidate prediction mode list may be created or updated according to at least one selected prediction mode. Optionally, after determining the second generation value of the default prediction mode, the second generation value of the neighbor block prediction mode, and the second generation value of the non-neighbor block prediction mode, the second generation values ​​may be arranged from small to large, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode may be selected and determined based on the arranged second generation values. For example, the prediction mode corresponding to the smallest second generation value (such as the default prediction mode) may be selected. Optionally, a candidate prediction mode list may be created or updated based on at least one selected prediction mode. Optionally, for method 1, it may include obtaining a cost tag in a bitstream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, calculating a cost value based on the selected first cost calculation method or the second cost calculation method and the neighbor block pixels, determining at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode based on the cost value, and creating or updating a candidate prediction mode list based on at least one of the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode, or the time domain prediction mode. Optionally, for the second method, it may include obtaining a cost tag in a code stream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, and calculating a cost value according to the selected first cost calculation method or second cost calculation method and the neighboring block pixels. Determine at least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block. Optionally, for the third method, it may include obtaining a cost tag in a code stream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, and calculating a cost value according to the selected first cost calculation method or the second cost calculation method and the neighboring block pixels. Determine at least one of a cross-component prediction mode of a neighboring block, a cross-component prediction mode of a non-neighboring block, and a time-domain prediction mode according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of a cross-component prediction mode of a neighboring block, a cross-component prediction mode of a non-neighboring block, and a time-domain prediction mode. Optionally, for method four, it can include obtaining a cost mark in the code stream, when the cost mark is a first value, selecting a first cost calculation method, when the cost mark is a second value, selecting a second cost calculation method, calculating a cost value based on the selected first cost calculation method or second cost calculation method and neighboring block pixels, determining a prediction mode of the luminance component of the neighboring block and at least one of the prediction modes of the non-neighboring block based on the cost value, and determining or obtaining a candidate prediction mode list for the current chrominance component based on at least one of the prediction modes of the luminance component of the neighboring block and the non-neighboring block prediction modes. Optionally, for method five, it can include obtaining a cost mark in the code stream, when the cost mark is a first value, selecting a first cost calculation method, when the cost mark is a second value, selecting a second cost calculation method, calculating a cost value based on the selected first cost calculation method or second cost calculation method and neighbor block pixels, determining at least one of a prediction mode of a luminance component of the neighbor block, a non-neighbor block prediction mode, and a time domain prediction mode based on the cost value, and determining or obtaining a candidate prediction mode list for the current chrominance component based on at least one of a prediction mode of a luminance component of the neighbor block, a non-neighbor block prediction mode, and a time domain prediction mode. Optionally, for method six, it can include obtaining a cost mark in the code stream, when the cost mark is a first value, selecting a first cost calculation method, when the cost mark is a second value, selecting a second cost calculation method, calculating a cost value based on the selected first cost calculation method or second cost calculation method, and neighboring block pixels, determining at least one of a default prediction mode, a time domain prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode based on the cost value, and creating or updating a candidate prediction mode list based on at least one of the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode. Optionally, for method 7, it may include obtaining a cost tag in a code stream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, and calculating a cost value according to the selected first cost calculation method or second cost calculation method and neighboring block pixels. Determine at least one of a cross-component prediction mode of an adjacent block and a cross-component prediction mode of a non-adjacent block according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of the cross-component prediction mode of an adjacent block and the cross-component prediction mode of a non-adjacent block. Optionally, for mode eight, it may include obtaining a cost tag in a code stream, selecting a first cost calculation mode when the cost tag is a first value, and selecting a second cost calculation mode when the cost tag is a second value, and calculating a cost value according to the selected first cost calculation mode or second cost calculation mode and neighboring block pixels. Determine at least one of a cross-component prediction mode of an adjacent block, a cross-component prediction mode of a non-adjacent block, and a time-domain prediction mode according to the cost value, and determine or obtain a candidate prediction mode list according to at least one of a cross-component prediction mode of an adjacent block, a cross-component prediction mode of a non-adjacent block, and a time-domain prediction mode. Optionally, for mode nine, it may include obtaining a cost tag in a code stream, selecting a first cost calculation mode when the cost tag is a first value, and selecting a second cost calculation mode when the cost tag is a second value, and calculating a cost value based on the selected first cost calculation mode or second cost calculation mode and neighboring block pixels. Determine at least one of the prediction modes of the brightness component of the adjacent block and the prediction modes of the non-adjacent block based on the cost value, and determine or obtain a candidate prediction mode list of the current chrominance component based on at least one of the prediction modes of the brightness component of the adjacent block and the prediction modes of the non-adjacent block. Optionally, for method 10, it may include obtaining a cost tag in a bitstream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, and calculating a cost value based on the selected first cost calculation method or the second cost calculation method and pixels of neighboring blocks. Determine at least one of a prediction mode of a luminance component of an adjacent block, a prediction mode of a non-adjacent block, and a time domain prediction mode based on the cost value, and determine or obtain a candidate prediction mode list of a current chrominance component based on at least one of a prediction mode of a luminance component of an adjacent block, a prediction mode of a non-adjacent block, and a time domain prediction mode. Optionally, for the twelveth method, it can include obtaining a cost tag in the code stream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, and calculating a cost value according to the selected first cost calculation method or the second cost calculation method and the neighbor block pixels. Determine at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, and a time domain prediction mode according to the cost value, and create or update a candidate prediction mode list according to the at least one prediction mode. Optionally, for method 1, the method may include obtaining a cost tag in a bitstream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, and calculating a cost value according to the selected first cost calculation method or the second cost calculation method and the neighboring block pixels. Determine at least one prediction mode of a temporal prediction mode of displacement / offset and a prediction mode of a historical record according to the cost value, and prediction mode creation or a list of functional candidate prediction modes. In this embodiment, when the processing device is a decoding end and the cost mark obtained in the code stream is a first value, the first cost calculation method is selected, and when the cost mark is a second value, the second cost calculation method is selected, and the cost value is calculated according to the selected first cost calculation method or the second cost calculation method, and the neighbor block pixel, at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode is determined according to the cost value, thereby ensuring the effectiveness of the determined prediction mode, so as to select a suitable prediction mode to construct a candidate prediction mode list. Method 6, calculates at least two different third-generation values ​​corresponding to the default prediction mode according to the neighbor block pixels, calculates at least two different third-generation values ​​corresponding to the neighbor block prediction mode, calculates at least two different third-generation values ​​corresponding to the non-neighbor block prediction mode, determines or obtains the fourth-generation value according to the at least two different third-generation values, and determines at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode according to the fourth-generation value. Optionally, at least two different third cost values ​​are two different cost values ​​calculated using different cost functions, such as two different cost values ​​of SAD and SATD. Optionally, the same cost function may be used to perform multiple cost value calculations to obtain at least two different third cost values. However, when the same cost function is used to perform multiple cost value calculations, the neighbor block pixels and / or weight parameters of the neighbor block pixels input into the cost function each time are different. Optionally, at least two different third generation values ​​can be calculated using the first cost function and the second cost function. Optionally, the cost value and reconstruction value of the neighbor block pixel when the default prediction mode is adopted can be calculated, and the cost value and reconstruction value are input into the first cost function for calculation to obtain the third generation value of the default prediction mode. The cost value and reconstruction value are input into the second cost function for calculation to obtain another third generation value of the default prediction mode. The cost value and reconstruction value of the neighbor block pixel when the neighbor block prediction mode is adopted are calculated, and the cost value and reconstruction value are input into the first cost function for calculation to obtain the third generation value of the neighbor block prediction mode. The cost value and reconstruction value are input into the second cost function for calculation to obtain another third generation value of the neighbor block prediction mode. The cost value and reconstruction value of the neighbor block pixel when the non-neighbor block prediction mode is adopted are calculated, and the cost value and reconstruction value are input into the first cost function for calculation to obtain the third generation value of the non-neighbor block prediction mode. The cost value and reconstruction value are input into the second cost function for calculation to obtain another third generation value of the non-neighbor block prediction mode. Optionally, after determining at least two different third-generation values, the at least two different third-generation values ​​can be calculated to obtain the fourth-generation value of the prediction model. For example, weighted calculation is performed, different weights are set for at least two different third-generation values, and then weighted calculation is performed based on the weights and at least two third-generation values ​​to obtain the fourth-generation value, such as cost4=w3a*cost3a+w3b*cost3b, where cost4 is the fourth-generation value, cost3a is a third-generation value, w3a is the weight of cost3a, cost3b is another third-generation value, and w3b is the weight of cost3b. Alternatively, at least two third-generation values ​​are screened and the largest or smallest cost value is selected as the fourth-generation value. Optionally, after determining the fourth generation value of at least one prediction mode, the fourth generation values ​​may be arranged from small to large, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode may be selected and determined according to the arranged fourth generation values. Optionally, a candidate prediction mode list may be created or updated according to the selected at least one prediction mode. Optionally, for method one, it can include respectively calculating at least two different third-generation values ​​corresponding to the default prediction mode, neighbor block prediction mode, non-neighbor block prediction mode or time domain prediction mode, determining or obtaining a fourth-generation value based on the at least two different third-generation values, determining at least one of the default prediction mode, neighbor block prediction mode, non-neighbor block prediction mode or time domain prediction mode based on the fourth-generation value, and creating or updating a candidate prediction mode list based on at least one of the default prediction mode, neighbor block prediction mode, non-neighbor block prediction mode or time domain prediction mode. Optionally, for the second method, it can include calculating at least two different third-generation values ​​of the cross-component prediction mode of the neighboring block, calculating at least two different third-generation values ​​of the cross-component prediction mode of the non-neighboring block, determining or obtaining a fourth-generation value based on the at least two different third-generation values, determining at least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block based on the fourth-generation value, and determining or obtaining a candidate prediction mode list based on at least one of the cross-component prediction mode of the neighboring block and the cross-component prediction mode of the non-neighboring block. Optionally, for method three, it can include calculating at least two different third-generation values ​​of the cross-component prediction mode of the neighboring block, at least two different third-generation values ​​of the cross-component prediction mode of the non-neighboring block, and at least two different third-generation values ​​of the time-domain prediction mode. Determine or obtain a fourth-generation value based on the at least two different third-generation values. Determine at least one of the cross-component prediction mode of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode based on the fourth-generation value, and determine or obtain a candidate prediction mode list based on at least one of the cross-component prediction mode of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode. Optionally, for method four, it can include respectively calculating at least two different third-generation values ​​corresponding to the prediction mode of the brightness component of the neighboring block and the prediction mode of the non-neighboring block, determining or obtaining a fourth-generation value based on the at least two different third-generation values, determining at least one of the prediction mode of the brightness component of the neighboring block and the prediction mode of the non-neighboring block based on the fourth-generation value, and determining or obtaining a candidate prediction mode list for the current chrominance component based on the prediction mode of the brightness component of the neighboring block and at least one of the prediction modes of the non-neighboring block. Optionally, for the fifth method, it may include respectively calculating at least two different third-generation values ​​corresponding to the prediction mode of the brightness component of the neighboring block, the prediction mode of the non-neighboring block, and the time-domain prediction mode, and determining or obtaining the fourth-generation value according to the at least two different third-generation values. At least one of the prediction mode of the brightness component of the neighboring block, the prediction mode of the non-neighboring block, and the time-domain prediction mode is determined according to the fourth-generation value, and a candidate prediction mode list of the current chrominance component is determined or obtained according to at least one of the prediction mode of the brightness component of the neighboring block, the prediction mode of the non-neighboring block, and the time-domain prediction mode. Optionally, for method six, it may include respectively calculating at least two different third-generation values ​​corresponding to the default prediction mode, the time-domain prediction mode, the adjacent block prediction mode, and the non-adjacent block prediction mode, and determining or obtaining the fourth-generation value according to the at least two different third-generation values. At least one of the default prediction mode, the time-domain prediction mode, the adjacent block prediction mode, and / or the non-adjacent block prediction mode is determined according to the fourth-generation value, and a candidate prediction mode list is created or updated according to at least one of the default prediction mode, the time-domain prediction mode, the adjacent block prediction mode, and / or the non-adjacent block prediction mode. Optionally, for method seven, it can include calculating at least two different third-generation values ​​of the cross-component prediction mode of the adjacent block, and calculating at least two different third-generation values ​​of the cross-component prediction mode of the non-adjacent block. Determine or obtain a fourth value based on the at least two different third-generation values. Determine at least one of the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block based on the fourth-generation value, and determine or obtain a candidate prediction mode list based on at least one of the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block. Optionally, for the eighth method, at least two different third generation values ​​of the cross-component prediction mode of the adjacent blocks may be calculated, at least two different third generation values ​​of the cross-component prediction mode of the non-adjacent blocks may be calculated, and at least two different third generation values ​​of the time domain prediction mode may be calculated. The third generation value determines or obtains the fourth generation value. At least one of the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode is determined according to the fourth generation value, and the candidate prediction mode list is determined or obtained according to at least one of the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode. Optionally, for method nine, it can include respectively calculating at least two different third-generation values ​​corresponding to the prediction mode of the brightness component of the adjacent block and the prediction mode of the non-adjacent block, determining a fourth-generation value based on the at least two different third-generation values, determining the prediction mode of the brightness component of the adjacent block and at least one of the prediction modes of the non-adjacent block based on the fourth-generation value, and determining or obtaining a candidate prediction mode list for the current chrominance component based on the prediction mode of the brightness component of the adjacent block and at least one of the prediction modes of the non-adjacent block. Optionally, for method ten, it can include respectively calculating at least two different third-generation values ​​corresponding to the prediction mode of the brightness component of the adjacent block, the prediction mode of the non-adjacent block, and the time-domain prediction mode, and determining or obtaining the fourth-generation value according to the at least two different third-generation values. Determine at least one of the prediction mode of the brightness component of the adjacent block, the prediction mode of the non-adjacent block, and the time-domain prediction mode according to the fourth-generation value, and determine or obtain the candidate prediction mode list of the current chrominance component according to at least one of the prediction mode of the brightness component of the adjacent block, the prediction mode of the non-adjacent block, and the time-domain prediction mode. Optionally, for the twelfth method, it can include calculating at least two different third-generation values ​​corresponding to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode, and the time domain prediction mode according to the neighbor block pixels. Determine the fourth-generation value according to the at least two different third-generation values. Determine at least one prediction mode among the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode, and the time domain prediction mode according to the fourth-generation value, and create or update the candidate prediction mode list according to the at least one prediction mode. Optionally, for method 1, it may include obtaining a temporal prediction mode of displacement / offset according to a motion vector, determining a prediction mode of a historical record, calculating at least two different third-generation values ​​of the temporal prediction mode of displacement / offset, and calculating at least two different third-generation values ​​of the prediction mode of the historical record, and determining a fourth-generation value according to the at least two different third-generation values. At least one prediction mode of the temporal prediction mode of displacement / offset and the prediction mode of the historical record is determined according to the fourth-generation value, and a functional candidate prediction mode list is created or generated according to at least one prediction mode. In this embodiment, at least two different third-generation values ​​corresponding to the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode are calculated respectively according to the neighbor block pixels, a fourth-generation value is determined or obtained according to the at least two different third-generation values, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode is determined according to the fourth-generation value, thereby ensuring the effectiveness of the determined prediction mode, so as to select a suitable prediction mode to construct a candidate prediction mode list. Third embodiment Based on any of the above embodiments, a third embodiment is proposed, please refer to FIG. 12 . In this embodiment, method eleven, creating or updating a candidate prediction mode list according to at least one prediction mode, includes at least one of methods thirteen to fifteen. Method 13: adding at least one prediction mode to a preset prediction mode list, and creating or updating a candidate prediction mode list; In this embodiment, at least one prediction mode may be determined according to the method in any of the above embodiments. Optionally, when creating a candidate prediction mode list, an empty table may be first constructed as a preset prediction mode list, and then at least one prediction mode may be added to the preset prediction mode list to obtain a candidate prediction mode list. Optionally, the prediction mode list may be a list, a table, an array, or data in a preset format, which is not limited here. Optionally, if it is necessary to construct at least one level of candidate prediction mode lists, at least one table may be constructed, each table may be used as a level of candidate prediction mode lists, and then at least one prediction mode may be added to each candidate prediction mode list. Optionally, when updating the candidate prediction mode list, a candidate prediction mode list constructed in advance may be determined, and then at least one prediction mode may be added to the candidate prediction mode list to update the candidate prediction mode list. Optionally, when updating the candidate prediction mode list, at least one prediction mode may be inserted into a specific position in the candidate prediction mode list, or an existing prediction mode in the candidate prediction mode list may be replaced with at least one determined prediction mode. A new list may also be created based on at least one prediction mode, and then the candidate prediction mode list may be updated based on the new list. Mode 14: sorting the determined at least one prediction mode according to a preset sequence order, and creating or updating a candidate prediction mode list; Optionally, at least one prediction mode may be determined from a default prediction mode, a neighbor block prediction mode, and / or a non-neighbor block prediction mode. At least one prediction mode may also be determined from a default prediction mode, an adjacent block prediction mode, and / or a non-adjacent block prediction mode. Optionally, at least one prediction mode may be determined based on SAD values ​​corresponding to each prediction mode, or may be randomly selected, or may be determined based on a preset rule. Optionally, the preset sequence order may be an order set in advance by a user. The preset sequence order may also be determined according to the location of the block using the prediction mode. The preset sequence order may also be determined according to the prediction effect of each prediction mode. The preset sequence order may also be determined according to the distance or SAD value or statistical number corresponding to each prediction mode, etc. Optionally, at least one of the determined prediction modes may be sorted in a preset sequence order to obtain a mode sequence corresponding to each prediction mode. When creating a prediction mode list, an empty table may be first constructed as a preset prediction mode list, and then the mode sequence may be added to the preset prediction mode list to obtain a candidate prediction mode list. Optionally, if it is necessary to construct at least one level of candidate prediction mode list, at least one table can be constructed, each table can be used as a candidate prediction mode list of one level, and then the mode sequence can be divided into sub-mode sequences with an equal number of candidate prediction mode lists, and each sub-mode sequence can be added to each candidate prediction mode list in turn. Optionally, when updating the candidate prediction mode list, a candidate prediction mode list constructed in advance may be determined, and then the mode sequence is added to the candidate prediction mode list to update the candidate prediction mode list. Optionally, when updating the candidate prediction mode list, at least one prediction mode in the mode sequence may be inserted into a specific position in the candidate prediction mode list, or an existing prediction mode in the candidate prediction mode list may be replaced with at least one prediction mode in the mode sequence. A new list may also be created based on the mode sequence, and the candidate prediction mode list may be updated based on the new list. Mode 15: Determine part or all of the prediction mode lists according to at least one prediction mode, and create or update the candidate prediction mode list according to part or all of the prediction mode lists. Optionally, a partial prediction mode list may be created first, and then the candidate prediction mode list may be created or updated based on the partial prediction mode list. Alternatively, a full prediction mode list may be created first, and then the candidate prediction mode list may be created or updated based on the full prediction mode list. Optionally, when creating a partial prediction mode list or a complete prediction mode list, at least one prediction mode may be first determined among the default prediction mode, the neighbor block prediction mode and / or the non-neighbor block prediction mode, and then the partial prediction mode list or the complete prediction mode list may be created based on the at least one prediction mode. It is also possible to determine at least one prediction mode among the default prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode, and then the partial prediction mode list or the complete prediction mode list may be created based on the at least one prediction mode. Optionally, all the determined prediction modes are sorted and added to a table to obtain a complete prediction mode list, or some prediction modes are selected from the at least one determined prediction mode and added to a table to obtain a partial prediction mode list. Optionally, when creating the candidate prediction mode list, part of the prediction mode list or the entire prediction mode list may be directly used as the candidate prediction mode list. When updating the candidate prediction mode list, the existing candidate prediction mode list may be updated according to the part of the prediction mode list or the entire prediction mode list. Optionally, for the fifteenth method, part or all of the prediction mode list is determined according to at least one prediction mode, which may include at least one of the sixteenth to the twentyth methods. Mode 16: Calculate the cost of at least one prediction mode according to the calculation rule, and determine part or all of the prediction mode list according to the cost of at least one prediction mode; Optionally, after determining at least one prediction mode according to the SAD value, the cost or index of at least one prediction mode can be calculated according to the calculation rule, and the cost is explained later. For example, the cost of each prediction mode is calculated according to the calculation rule. Optionally, the calculation rule can be any calculation rule set in advance by the user, which is not limited here. Optionally, after determining the cost of at least one prediction mode, at least one prediction mode may be screened according to the cost, such as selecting a prediction mode with a smaller cost. Or a prediction mode corresponding to a cost less than a preset cost threshold may be selected. A partial or full prediction mode list may be constructed based on the screened prediction modes. For example, a mode list may be obtained by sorting all the screened prediction modes, and the list may be used as the full prediction mode list. Alternatively, the list may be used as a partial prediction mode list, and then other partial prediction mode lists may be obtained according to other rules, and a candidate prediction mode list may be created or updated by integrating all partial prediction mode lists or the full prediction mode list. Method 17: inputting at least one prediction mode into a neural network to obtain an output result, and determining part or all of the prediction mode list according to the output result; Optionally, after determining at least one prediction mode, at least one prediction mode can be input into a pre-set neural network to obtain an output result, and a partial or full prediction mode list can be constructed based on the prediction mode corresponding to the output result. Optionally, the neural network can be a nonlinear algorithm or module, such as matrix weighted intra prediction technology (Matrix Weighted Intra Prediction, MIP), and the target neural network can include at least one of the following neural networks, such as: Convolutional Neural Network (CNN), Residual neural network (ResNet), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Three-dimensional Convolutional Neural Network (3D-CNN), Fully Connected Neural Network (FCNN), etc. For example, as shown in FIG13 , in a frame of image blocks, there are blocks to be predicted, adjacent blocks adjacent to the blocks to be predicted that have been coded and decoded, and non-adjacent non-adjacent blocks. Optionally, the blocks to be predicted may be the current block Pred, the adjacent blocks may be TL1, T1, L1, LB1, and the non-adjacent blocks may be TL3, T2, TR2, TL2, TR1, L2, and LB2. A list with prediction mode indexes is constructed based on the prediction modes corresponding to the adjacent blocks and the prediction modes corresponding to the non-adjacent blocks. A list with reference pixel blocks is constructed based on the pixel blocks corresponding to the adjacent blocks and the pixel blocks corresponding to the non-adjacent blocks, and a neural network is introduced, in which various modules are provided, such as: Convolutional layer conv, 1x1, 32; Convolution layer conv, 3x3, 32; Pooling layer Pooling; Convolutional layer conv, 1x1, 64; convolutional layer conv, 3x3, 64, pooling layer Pooling; Convolutional layer conv, 1x1, 128; Convolution layer conv, 3x3, 128, pooling layer Pooling; Fully connected layer FC, 1024; Fully connected layer FC, 1024; Fully connected layer FC, 16; The prediction mode index and reference pixel block are input into the neural network for mode probability (unnormalized) processing, sorting and screening, and the candidate mode list is output. If the prediction mode is obtained from the encoded and decoded block, including the prediction mode of the adjacent block and the prediction mode of the non-adjacent block. If there are 15 prediction modes in total, the corresponding mode index numbers are 1 to 15. If for the current block in Figure 13, a total of 10 prediction modes (1, 13, 7, 5, 9, 12, 4, 15, 8, 2) are obtained. Then, these 10 prediction modes and the blocks they are in are integrated and converted into an NxM regular matrix, which is a data form that can be recognized by the convolutional neural network. Optionally, a padding operation is performed on matrix elements for which no prediction mode exists (the block where the matrix elements are located has not been encoded or decoded), such as a zero-padding process, to obtain the prediction mode index matrix shown in FIG. 13 . Optionally, similar to the prediction mode index matrix, pixels are obtained from the encoded and decoded block, i.e., neighbor block pixels, and filling operations are performed on matrix elements where pixels do not exist (the block where they exist has not yet been encoded and decoded), such as padding with 0, to obtain the reference pixel block shown in FIG. 13 . Optionally, the prediction mode index matrix and the reference pixel block are input into a neural network, which is provided with 12 layers, optionally, 6 CNN layers (convolution kernel size is 1x1 or 3x3, the number of output channels is 32 or 64 or 128), 3 pooling layers, 3 fully connected layers (FC), and the final output is 15 unnormalized probabilities p1-p15 (representing the probabilities of using 15 candidate modes). The above 15 probabilities p1-p15 are sorted from large to small, for example, the top 5 largest probabilities are taken to construct a candidate prediction mode list, that is, the candidate list [2,12,3,1,7] shown in Figure 13. Method 18: Input at least one prediction model into a query table for query, obtain query results, and determine part or all of the predictions based on the query results. List of modes; Optionally, at least one prediction mode may be processed first, such as sorting, deleting, etc., and then the processed prediction mode may be matched with each prediction mode in the query table to obtain a query result, such as a matched prediction mode, etc. Then, a partial or complete prediction mode list may be constructed based on the matched prediction mode. For example, as shown in FIG14, in a frame of image blocks, there are blocks to be predicted, adjacent blocks adjacent to the blocks to be predicted and have been coded and decoded, and non-adjacent non-adjacent blocks. Optionally, the block to be predicted may be the current block Pred, the adjacent blocks may be TL1, T1, L1, LB1, and the non-adjacent blocks may be TL3, T2, TR2, TL2, TR1, L2, and LB2. A list with prediction mode indexes is constructed based on the prediction modes corresponding to the adjacent blocks and the prediction modes corresponding to the non-adjacent blocks. The prediction mode indexes are processed by the preprocessing module, and then queried and matched according to the query table by the table lookup / pattern matching module to obtain a candidate mode list. Optionally, if a prediction mode is obtained from a coded and decoded block, it includes the prediction mode of the adjacent blocks and the prediction mode of the non-adjacent blocks. If there are 16 prediction modes obtained, the corresponding mode indexes are numbered from 1 to 16. For the current block in Figure 14, a total of 9 prediction modes (3, 14, 2, 7, 9, 6, 4, 11, 16) are obtained. These 9 prediction modes are processed by the preprocessing module, such as sorting and deleting, to obtain a sequence of 5 prediction modes [16, 2, 9, 11, 6]. Then, the table lookup / pattern matching module performs table lookup or pattern matching in the query table to obtain a candidate mode list. Alternatively, the query table may be a table pre-stored in the system. Method 19: Perform pattern matching based on at least one prediction pattern to determine a partial or complete prediction pattern list. Optionally, a pattern matching rule may be determined first, and at least one prediction pattern may be matched with the pattern matching rule to determine part or all of the prediction pattern list. For example, you can first determine the pattern sequence index corresponding to each prediction mode, such as 1, 2, 3, 4, etc. If the pattern matching rules include [1, xxx, 4], xxx represents the pattern sequence index corresponding to any prediction mode. If the pattern sequence is determined to be [1, 2, 3, 4] based on at least one prediction mode, it can be determined that at least one prediction mode matches the pattern matching rule. At this time, the prediction mode corresponding to the pattern sequence index [1, 2, 3, 4] can be inserted into the pattern list to determine part or all of the prediction mode list. If the pattern matching rules include [*, 10, *, 40, *], * represents the pattern sequence index corresponding to any prediction mode. If at least one prediction mode includes a pattern sequence index of

[0020] If a prediction mode is specified, the prediction mode corresponding to the mode sequence index

[0020] can be inserted into the mode list to determine part or all of the prediction mode list. Method 20: Input at least one prediction model into the mathematical model, and determine part or all of the prediction model list according to the output results of the mathematical model. Optionally, a mathematical model may be constructed and set up first, at least one prediction mode may be input into the mathematical model for screening, a prediction mode may be output, and a partial or complete prediction mode list may be constructed based on the output prediction mode. For example, as shown in FIG15 , a prediction module is obtained from the encoded and decoded block. The prediction modes of adjacent blocks are MMLM_CHROMA (CCCM) and MDLM_L (CCLM), and the prediction modes of non-adjacent blocks are MMLM_CHROMA (CCLM) and MDLM_T (GL-CCCM). Optionally, some blocks may not use the cross-component prediction mode, and different blocks may use exactly the same prediction mode. Assume that the LM_CHROMA (CCLM) mode is the default mode. For the current block Pred, a total of 5 candidate modes, i.e., prediction modes, are obtained. For these five prediction modes, the cost COST can be calculated for each mode in the mathematical model based on data, model parameters, transformation rules, etc. For example, SATD (Sum of Absolute Transformed Difference) is calculated for each mode, which first performs a Hadamard transform on the residual and then calculates the sum of the absolute values. Five COST values ​​for the five modes are obtained as shown in FIG15 . That is: LM_CHROMA(CCLM)=23043, MMLM_CHROMA(CCCM)=132454, MDLM_L(CCLM)=13232, MMLM_CHROMA(CCLM)=433433, MDLM_T(GL-CCCM)=54545. Then, these five cost values ​​are sorted from small to large, and the sorted candidate mode list is obtained as follows: MDLM_L(CCLM), LM_CHROMA(CCLM), MDLM_T(GL-CCCM), MMLM_CHROMA(CCCM), MMLM_CHROMA(CCLM). Optionally, the higher ranked modes are represented using fewer bits to increase the coding rate. Optionally, method one includes: At least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode is used as a first prediction mode, and a candidate prediction mode list is created or updated according to the first prediction mode. Optionally, at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a cross-component prediction mode of a neighbor block, a non-neighbor block prediction mode, and a cross-component prediction mode of a non-neighbor block is used as the first prediction mode. Optionally, creating or updating a candidate prediction mode list according to the first prediction mode includes at least one of the following: adding the first prediction mode to a preset prediction mode list to create or update a candidate prediction mode list; or, sorting the determined first prediction mode in a preset sequence order to create or update a candidate prediction mode list; or, determining part or all of the prediction mode list according to the first prediction mode, and creating or updating the candidate prediction mode list according to part or all of the prediction mode list. Optionally, determining part or all of the prediction mode list according to the first prediction mode includes at least one of the following: calculating the cost of the first prediction mode according to a calculation rule, and determining part or all of the prediction mode list according to the cost of the first prediction mode; or, inputting the first prediction mode into a neural network to obtain an output result, and determining part or all of the prediction mode list according to the output result; or, inputting the first prediction mode into a query table for querying to obtain a query result, and determining part or all of the prediction mode list according to the query result; or, performing pattern matching according to the first prediction mode to determine part or all of the prediction mode list, or, performing pattern matching on the first prediction mode to determine part or all of the prediction mode list. Optionally, the specific operation process of the specific detailed steps of method one can refer to the specific detailed step process of method eleven in this embodiment, and will not be repeated here. Optionally, the second method includes at least one of the following: Add the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block to the preset prediction mode list, and create or update the candidate prediction mode list; or, sort the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block in a preset sequence order, and create or update the candidate prediction mode list; or, determine part or all of the prediction mode list according to the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block, and create or update the candidate prediction mode list according to part or all of the prediction mode list. Optionally, determining part or all of the prediction mode list according to the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block includes at least one of the following: The cost of the cross-component prediction mode of the neighbor block and the cost of the cross-component prediction mode of the non-neighbor block are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the cross-component prediction mode of the neighbor block and the cost of the cross-component prediction mode of the non-neighbor block; or, the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block are input into the neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or The cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block are input into a query table for query, and the query results are obtained, and part or all of the prediction mode list is determined according to the query results; or the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of the second method can refer to the specific detailed step process of the eleventh method in this embodiment, and will not be repeated here. Optionally, the third method includes at least one of the following: Add the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode to the preset prediction mode list, and create or update the candidate prediction mode list; or, sort the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode in a preset sequence order, and create or update the candidate prediction mode list; or, determine part or all of the prediction mode list based on the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode, and create or update the candidate prediction mode list based on part or all of the prediction mode list. Optionally, according to the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode, determine that part or all of the prediction mode list includes at least one of the following: The cost of the cross-component prediction mode of the neighbor block, the cost of the cross-component prediction mode of the non-neighbor block and the cost of the time-domain prediction mode are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the cross-component prediction mode of the neighbor block, the cost of the cross-component prediction mode of the non-neighbor block and the cost of the time-domain prediction mode; or, the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block and the time-domain prediction mode are input into a neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block and the time-domain prediction mode are input into a query table for query to obtain a query result, and part or all of the prediction mode list is determined according to the query result; or, the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block and the time-domain prediction mode are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method three can refer to the specific detailed step process of method eleven in this embodiment, and will not be repeated here. Optionally, method 4 includes at least one of the following: Add the prediction mode of the luminance component of the neighboring block and the prediction mode of the non-neighboring block to the preset prediction mode list, and create or update the candidate prediction mode list of the current chrominance component; or, sort the prediction mode of the luminance component of the neighboring block and the non-neighboring block prediction mode in a preset sequence order, and create or update the candidate prediction mode list of the current chrominance component; or, determine part or all of the prediction mode list according to the prediction mode of the luminance component of the neighboring block and the non-neighboring block prediction mode, and create or update the candidate prediction mode list of the current chrominance component according to part or all of the prediction mode list. Optionally, determining part or all of the prediction mode list according to the prediction mode of the luminance component of the neighboring block and the non-neighboring block prediction mode includes at least one of the following: The cost of the prediction mode of the brightness component of the neighboring block and the cost of the prediction mode of the non-neighboring block are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the prediction mode of the brightness component of the neighboring block and the cost of the prediction mode of the non-neighboring block; or, the prediction mode of the brightness component of the neighboring block and the non-neighboring block prediction mode are input into a neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the prediction mode of the brightness component of the neighboring block and the non-neighboring block prediction mode are input into a query table for query to obtain a query result, and part or all of the prediction mode list is determined according to the query result; or, the prediction mode of the brightness component of the neighboring block and the non-neighboring block prediction mode are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method 4 can refer to the specific detailed step process of method 11 in this embodiment, and will not be repeated here. Optionally, method five includes at least one of the following: Add the prediction mode of the brightness component of the neighbor block, the prediction mode of the non-neighbor block, and the time domain prediction mode to the preset prediction mode list, and create or update the candidate prediction mode list of the current chrominance component; or, sort the prediction mode of the brightness component of the neighbor block, the prediction mode of the non-neighbor block, and the time domain prediction mode in a preset sequence order, and create or update the candidate prediction mode list of the current chrominance component; or, determine part or all of the prediction mode list according to the prediction mode of the brightness component of the neighbor block, the prediction mode of the non-neighbor block, and the time domain prediction mode, and create or update the candidate prediction mode list of the current chrominance component according to part or all of the prediction mode list. Optionally, according to the prediction mode of the brightness component of the neighbor block, the prediction mode of the non-neighbor block, and the time domain prediction mode, determine that part or all of the prediction mode list includes at least one of the following: The cost of the prediction mode of the brightness component of the neighbor block, the cost of the prediction mode of the non-neighbor block and the cost of the time domain prediction mode are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the prediction mode of the brightness component of the neighbor block, the cost of the prediction mode of the non-neighbor block and the cost of the time domain prediction mode; or, the prediction mode of the brightness component of the neighbor block, the non-neighbor block prediction mode and the time domain prediction mode are input into a neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the prediction mode of the brightness component of the neighbor block, the non-neighbor block prediction mode and the time domain prediction mode are input into a query table for query to obtain a query result, and part or all of the prediction mode list is determined according to the query result; or, the prediction mode of the brightness component of the neighbor block, the non-neighbor block prediction mode and the time domain prediction mode are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method five can refer to the specific detailed step process of method eleven in this embodiment, and will not be repeated here. Optionally, the sixth method includes at least one of the following: Add the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode to the preset prediction mode list to create or update the candidate prediction mode list; or, sort the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode in a preset sequence order to create or update the candidate prediction mode list; or, determine part or all of the prediction mode list according to the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode, and create or update the candidate prediction mode list according to part or all of the prediction mode list. Optionally, determining part or all of the prediction mode list according to the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode includes at least the following: One: The costs corresponding to the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the costs; or, the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode are input into the neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode are input into a query table for query, and the query result is obtained, and part or all of the prediction mode list is determined according to the query result; or, the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode are pattern matched to determine part or all of the prediction mode list; or, the default prediction mode, the time domain prediction mode, the adjacent block prediction mode and / or the non-adjacent block prediction mode are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method 6 can refer to the specific detailed step process of method 11 in this embodiment, and will not be repeated here. Optionally, method seven includes at least one of the following: Add the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block to the preset prediction mode list, and create or update the candidate prediction mode list; or, sort the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block in a preset sequence order, and create or update the candidate prediction mode list; or, determine part or all of the prediction mode list according to the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block, and create or update the candidate prediction mode list according to part or all of the prediction mode list. Optionally, determining part or all of the prediction mode list according to the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block includes at least one of the following: The cost of the cross-component prediction mode of the adjacent block and the cost of the cross-component prediction mode of the non-adjacent block are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the cross-component prediction mode of the adjacent block and the cost of the cross-component prediction mode of the non-adjacent block; or, the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block are input into a neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block are input into a query table for query to obtain a query result, and part or all of the prediction mode list is determined according to the query result; or, the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method seven can refer to the specific detailed step process of method eleven in this embodiment, and will not be repeated here. Optionally, the eighth method includes at least one of the following: Add the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode to the preset prediction mode list, and create or update the candidate prediction mode list; or, sort the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode in a preset sequence order, and create or update the candidate prediction mode list; or, determine part or all of the prediction mode list based on the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode, and create or update the candidate prediction mode list based on part or all of the prediction mode list. Optionally, according to the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode, determine that part or all of the prediction mode list includes at least one of the following: The cost of the cross-component prediction mode of the adjacent block, the cost of the cross-component prediction mode of the non-adjacent block and the cost of the time-domain prediction mode are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the cross-component prediction mode of the adjacent block, the cost of the cross-component prediction mode of the non-adjacent block and the cost of the time-domain prediction mode; or, the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block and the time-domain prediction mode are input into a neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block and the time-domain prediction mode are input into a query table for query to obtain a query result, and part or all of the prediction mode list is determined according to the query result; or, the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block and the time-domain prediction mode are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method eight can refer to the specific detailed step process of method eleven in this embodiment, and will not be repeated here. Optionally, the ninth method includes at least one of the following: Add the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block to the preset prediction mode list, and create or update the candidate prediction mode list of the current chrominance component; or, sort the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block in a preset sequence order, and create or update the candidate prediction mode list of the current chrominance component; or, determine part or all of the prediction mode list according to the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block, and create or update the candidate prediction mode list of the current chrominance component according to part or all of the prediction mode list. Optionally, determining part or all of the prediction mode list according to the prediction mode of the luminance component of the adjacent block and the prediction mode of the non-adjacent block includes at least one of the following: The cost of the prediction mode of the brightness component of the adjacent block and the cost of the prediction mode of the non-adjacent block are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the prediction mode of the brightness component of the adjacent block and the cost of the prediction mode of the non-adjacent block; or, the prediction mode of the brightness component of the adjacent block and the prediction mode of the non-adjacent block are input into a neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the prediction mode of the brightness component of the adjacent block and the prediction mode of the non-adjacent block are input into a query table for query to obtain a query result, and part or all of the prediction mode list is determined according to the query result; or, the prediction mode of the brightness component of the adjacent block and the prediction mode of the non-adjacent block are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method nine can refer to the specific detailed step process of method eleven in this embodiment, and will not be repeated here. Optionally, method 10 includes at least one of the following: Add the prediction mode of the brightness component of the adjacent block, the prediction mode of the non-adjacent block, and the temporal prediction mode to the preset prediction mode list, and create or update the current The candidate prediction mode list of the previous chroma component; or, the prediction mode of the luminance component of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode are sorted in a preset sequence order to create or update the candidate prediction mode list of the current chroma component; or, according to the prediction mode of the luminance component of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode, part or all of the prediction mode list is determined, and the candidate prediction mode list of the current chroma component is created or updated according to the part or all of the prediction mode list. Optionally, according to the prediction mode of the luminance component of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode, determining part or all of the prediction mode list includes at least one of the following: The cost of the prediction mode of the brightness component of the adjacent block, the cost of the prediction mode of the non-adjacent block and the cost of the time domain prediction mode are calculated according to the calculation rules, and part or all of the prediction mode list is determined according to the cost of the prediction mode of the brightness component of the adjacent block, the cost of the prediction mode of the non-adjacent block and the cost of the time domain prediction mode; or, the prediction mode of the brightness component of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode are input into a neural network to obtain an output result, and part or all of the prediction mode list is determined according to the output result; or, the prediction mode of the brightness component of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode are input into a query table for query to obtain a query result, and part or all of the prediction mode list is determined according to the query result; or, the prediction mode of the brightness component of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode are input into a mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method ten can refer to the specific detailed step process of method eleven in this embodiment, and will not be repeated here. Optionally, the twelve methods include at least one of the following: Add at least one prediction mode to a preset prediction mode list, and create or update a candidate prediction mode list; sort the determined at least one prediction mode in a preset sequence order, and create or update a candidate prediction mode list; determine part or all of the prediction mode list based on at least one prediction mode, and create or update the candidate prediction mode list based on part or all of the prediction mode list; optionally, determine part or all of the prediction mode list based on at least one prediction mode, including at least one of the following: calculate the cost of at least one prediction mode according to a calculation rule, and determine part or all of the prediction mode list based on the cost of at least one prediction mode; input at least one prediction mode into a neural network to obtain an output result, and determine part or all of the prediction mode list based on the output result; input at least one prediction mode into a query table for query, obtain a query result, and determine part or all of the prediction mode list based on the query result; perform pattern matching based on at least one prediction mode to determine part or all of the prediction mode list; input at least one prediction mode into a mathematical model, and determine part or all of the prediction mode list based on the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of the method 12 can refer to the specific detailed step process of the method 11 in this embodiment, and will not be repeated here. Optionally, method 1 includes at least one of the following: Add at least one prediction mode among the time domain prediction mode for obtaining displacement / offset according to the motion vector and the prediction mode based on the historical record to the preset prediction mode list, and create or update the candidate prediction mode list; sort the at least one determined prediction mode according to the preset sequence order, and create or update the candidate prediction mode list; determine part or all of the prediction mode list according to at least one prediction mode, and create or update the candidate prediction mode list according to part or all of the prediction mode list; optionally, determine part or all of the prediction mode list according to at least one prediction mode, including at least one of the following: calculate the cost of at least one prediction mode according to a calculation rule, and determine part or all of the prediction mode list according to the cost of at least one prediction mode; input at least one prediction mode into a neural network to obtain an output result, and determine part or all of the prediction mode list according to the output result; input at least one prediction mode into a query table for query, obtain a query result, and determine part or all of the prediction mode list according to the query result; perform pattern matching according to at least one prediction mode to determine part or all of the prediction mode list; input at least one prediction mode into a mathematical model, and determine part or all of the prediction mode list according to the output result of the mathematical model. Optionally, the specific operation process of the specific detailed steps of method 1 can refer to the specific detailed step process of method 11 in this embodiment, and will not be repeated here. In this embodiment, by creating or updating a candidate prediction mode list for at least one prediction mode, the above-mentioned method is adopted to enrich the method of determining or obtaining the candidate prediction mode list and ensure the validity of the determined or obtained candidate prediction mode list. Fourth embodiment, Based on any of the above embodiments, a fourth embodiment is proposed, referring to FIG. 16 . In this embodiment, the candidate prediction mode list includes at least one of the following: a first-level mode list, a second-level mode list, a first-level chroma prediction mode list, and a second-level chroma prediction mode list. Optionally, the candidate prediction mode column may include at least one level of mode list, such as a first level mode list and a second level mode list, but is not limited to the first level mode list and the second level mode list. At least one mode list of different levels may be set according to actual needs, or only one mode list may be set. At least one prediction mode is set in each mode list. Optionally, the candidate prediction mode list may include a candidate prediction mode list of the current chroma component, the candidate prediction mode list of the current chroma component includes at least one hierarchical mode list, the at least one hierarchical mode list may include a first-level chroma prediction mode list and a second-level chroma prediction mode list, but is not limited to the first-level chroma prediction mode list and the second-level chroma prediction mode list, and at least one chroma prediction mode list of different levels may be set according to actual needs. At least one prediction mode for chroma component prediction is set in each chroma prediction mode list. Optionally, the first-level chroma mode list and the second-level chroma mode list may include a prediction mode for chroma component prediction for the current block. The first-level mode list and the second-level mode list may include a prediction mode for chroma component prediction for the current block, and may also include a prediction mode for luminance component prediction for the current block, which is not limited here. Optionally, the first-level mode list, the second-level mode list, the first-level chroma prediction mode list and / or the second-level chroma prediction mode list can be a list, a table, an array, or data in a preset format, which is not limited here. Optionally, method eleven, creating or updating a candidate prediction mode list according to at least one prediction mode, may be method twenty-one or method twenty-two. Method 21: creating or updating a first-level pattern list according to at least one prediction mode, and determining or obtaining a second-level pattern list according to the first-level pattern list and other prediction modes outside the first-level pattern list; In this embodiment, after determining at least one prediction mode, a first-level mode list can be created based on the at least one prediction mode. Optionally, an empty table can be constructed, and a part of prediction modes with good prediction effects can be selected from at least one prediction mode and placed in the empty table as the first-level mode list, or the prediction modes with the closest distance can be selected and placed in the empty table as the first-level mode list. Then, other prediction modes other than the first-level mode list are selected from the at least one prediction mode determined to construct the second-level mode list. Optionally, other prediction modes can be prediction modes other than the prediction modes in the first-level mode list from the at least one prediction mode determined. For example, if at least one prediction mode includes three prediction modes, namely A, B and C, and prediction modes B and C exist in the first-level mode list. Then the other prediction mode is A, and at this time, the prediction mode A is included in the second-level mode list. Optionally, the existing first-level mode list and / or second-level mode list may be updated according to at least one prediction mode. Optionally, if at least one level of pattern lists are constructed, the pattern bit string of the first level pattern list is the shortest, followed by the second level, and so on, and the pattern bit string outside the pattern list is the longest. Method 22: Create or update a first-level chroma mode list according to at least one prediction mode, and determine or obtain a second-level chroma mode list according to the first-level chroma mode list and other prediction modes outside the first-level chroma mode list. In this embodiment, after determining at least one prediction mode, a first-level chromaticity mode list can be created based on the at least one prediction mode. Optionally, an empty table can be constructed, and a part of prediction modes with good prediction effects can be selected from at least one prediction mode and placed in the empty table as the first-level chromaticity mode list, or the prediction mode with the closest distance can be selected and placed in the empty table as the first-level chromaticity mode list. Then, other prediction modes other than the first-level mode list are selected from the at least one prediction mode determined to construct the second-level chromaticity mode list. Optionally, other prediction modes can be prediction modes other than the prediction modes in the first-level chromaticity mode list in the at least one prediction mode determined. For example, if at least one prediction mode includes three prediction modes, namely A, B and C, and prediction modes B and C exist in the first-level chromaticity mode list. Then the other prediction mode is A, and at this time, the prediction mode A is included in the second-level chromaticity mode list. Optionally, the existing first-level chroma mode list and / or second-level chroma mode list may be updated according to at least one prediction mode. Optionally, if at least one level of chroma mode lists is constructed, the pattern bit string of the first level chroma mode list is the shortest, followed by the second level, and so on, and the pattern bit string outside the chroma mode list is the longest. Optionally, method 1 includes at least one of the following: Create or update a first-level mode list according to at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode, and determine or obtain a second-level mode list according to the first-level mode list and other prediction modes outside the first-level mode list; A first-level chroma mode list is created or updated according to at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, the second method includes: A first-level chroma mode list is created or updated according to the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block, and a second-level chroma mode list is determined or obtained according to the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, method three includes: A first-level chroma mode list is created or updated according to the cross-component prediction mode of the neighboring block, the cross-component prediction mode of the non-neighboring block, and the time-domain prediction mode, and a second-level chroma mode list is determined or obtained according to the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, method 4 includes: A first-level chroma mode list is created or updated according to the prediction mode of the brightness component of the neighbor block and the prediction mode of the non-neighbor block, and a second-level chroma mode list is determined or obtained according to the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, method five includes: A first-level chroma mode list is created or updated according to the prediction mode of the brightness component of the neighbor block, the non-neighbor block prediction mode and the time domain prediction mode, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, the sixth method includes at least one of the following: Creating or updating a first-level mode list according to a default prediction mode, a temporal prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode, and determining or obtaining a second-level mode list according to the first-level mode list and other prediction modes outside the first-level mode list; A first-level chroma mode list is created or updated according to a default prediction mode, a time domain prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, method seven includes: Optionally, a first-level chroma mode list is created or updated according to the cross-component prediction mode of the adjacent blocks and the cross-component prediction mode of the non-adjacent blocks, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, method eight includes: Optionally, a first-level chroma mode list is created or updated based on the cross-component prediction mode of adjacent blocks, the cross-component prediction mode of non-adjacent blocks, and the time-domain prediction mode, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, method nine includes: Optionally, a first-level chroma mode list is created or updated according to the prediction mode of the brightness component of the neighboring block and the prediction mode of the non-neighboring block, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, method ten includes: Optionally, a first-level chroma mode list is created or updated according to the prediction mode of the brightness component of the neighboring block, the non-neighboring block prediction mode and the time domain prediction mode, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction models outside the first-level chroma mode list. Optionally, the twelve methods include at least one of the following: According to at least one prediction mode, a first-level mode list is created or updated, and the first-level mode list and other prediction modes outside the first-level mode list are used. He predicts patterns and determines or obtains a list of second-level patterns; According to at least one prediction mode, a first-level chroma mode list is created or updated, and according to the first-level chroma mode list and other prediction modes outside the first-level chroma mode list, a second-level chroma mode list is determined or obtained. Optionally, method 1 includes at least one of the following: Creating or updating a first-level mode list according to at least one of a temporal prediction mode for obtaining displacement / offset of a motion vector and a prediction mode based on a historical record, and determining or obtaining a second-level mode list according to the first-level mode list and other prediction modes outside the first-level mode list; A first-level chroma mode list is created or updated based on a time domain prediction mode for obtaining displacement / offset according to a motion vector and at least one of the prediction modes based on historical records, and a second-level chroma mode list is determined or obtained based on the first-level chroma mode list and other prediction modes outside the first-level chroma mode list. In this embodiment, since the candidate prediction mode list includes at least one item from the first-level mode list, the second-level mode list, the first-level chroma prediction mode list and the second-level chroma prediction mode list, it is possible to predict the prediction block through mode lists of different levels to increase the probability of using the mode list and reduce the prediction cost. Fifth embodiment Based on any of the above embodiments, a fifth embodiment is proposed, please refer to FIG. 17 . In this embodiment, step S20 includes: Step c, determining at least one target prediction mode in the candidate prediction mode list; Optionally, after constructing the candidate prediction mode list, a prediction mode can be selected in the candidate prediction mode list, and the selected prediction mode is used as the target prediction mode. Optionally, if a prediction mode is hit in the candidate prediction mode list, the hit prediction mode is used as the target prediction mode. The method of hitting the prediction mode in the candidate prediction mode list can adopt the existing method of hitting the prediction mode, which is not limited here. For example, a prediction mode that can achieve the minimum rate distortion effect is selected for hitting, etc. Step d: predicting the current block according to the at least one target prediction mode to determine or obtain a predicted block. Optionally, if a target prediction mode is determined, the current block is predicted directly according to the target prediction mode to determine or obtain a prediction module. If multiple target prediction modes are determined, a prediction order corresponding to each target prediction mode is determined, and the target prediction modes are selected in turn according to the prediction order to predict the current block to determine or obtain a prediction block. Optionally, the processing method comprises: The index corresponding to the target prediction mode and the prediction block are encoded. Optionally, at the encoding side, after predicting the current block through the target prediction mode and determining or obtaining the predicted block, it is necessary to determine the index corresponding to the target prediction mode. Optionally, each prediction mode is set to correspond to an index, and the terminal device can directly query the corresponding target prediction mode according to the index. Then, the index and prediction block corresponding to the target prediction mode are encoded. In this embodiment, the current block is predicted according to at least one target prediction mode determined in the candidate prediction mode list to determine or obtain a prediction block, and then the index and prediction block corresponding to the target prediction mode are encoded, thereby improving the efficiency of video encoding. Sixth embodiment Based on any of the above embodiments, a sixth embodiment is proposed, please refer to FIG. 18 . In this embodiment, the processing method includes: A decoding block is determined or obtained according to the prediction block. In this embodiment, at the decoding side, after the prediction block is determined or obtained, the decoding block can be determined or obtained according to the prediction block, for example, a series of processes such as filtering are performed on the prediction block to determine or generate the decoding block. Optionally, at the decoding end, the index of the candidate prediction mode list in the bitstream may be obtained, and the current block may be predicted based on the index and the candidate prediction mode list to determine or obtain the prediction block, and then the decoding block may be determined or obtained based on the prediction block. Optionally, the method for determining the candidate prediction mode list in the decoding end may refer to the method for creating or updating the candidate prediction mode list in any of the above embodiments, and is not limited here. Optionally, predicting the current block according to the index and the candidate prediction mode list to determine or obtain the predicted block includes: Determine a target prediction mode in the candidate prediction mode list according to the index; The current block is predicted according to the target prediction mode to determine or obtain the predicted block. Optionally, the candidate prediction mode list includes at least one hierarchical mode list, the at least one hierarchical mode list includes at least a first-level mode list and a second-level mode list, and determining a target prediction mode in the candidate prediction mode list according to an index includes: A target prediction mode in the first-level mode list and / or the second-level mode list is determined according to the index. Optionally, whether the first-level mode list is used is obtained from the bitstream, and if so, the index of the prediction mode in the first-level mode list is obtained, and the target prediction mode is obtained according to the index and the first-level mode list; and / or, if not, whether the second-level mode list is used is obtained from the bitstream, and if so, the index of the prediction mode in the second-level mode list is obtained, and the target prediction mode is obtained according to the index and the second-level mode list. For example, as shown in FIG19 , at the decoding end, a flag bit corresponding to the first-level list is obtained from the bitstream, and it is determined whether the first-level list is adopted. If so, that is, it is determined that the first-level list is adopted, the first-level list is determined or generated, and the index is obtained from the bitstream, and the target prediction mode corresponding to the index is determined in the determined or generated first-level list; and / or, if not, that is, it is determined that the first-level list is not adopted, the flag bit corresponding to the second-level list is obtained from the bitstream, and it is determined whether the second-level list is adopted. If so, that is, it is determined that the second-level list is adopted, the second-level list is determined or generated, and the index is obtained from the bitstream, and the target prediction mode corresponding to the latest obtained index is determined in the determined or generated second-level list; and / or, if not, that is, it is determined that the second-level list is not adopted, the index can be obtained from the bitstream, and the target prediction mode corresponding to the index can be determined in the remaining mode set except the first-level list and the second-level list. Optionally, the first level list is a first level pattern list. Optionally, the second level list is a second level pattern list. For another example, as shown in FIG20 , the flag bit corresponding to the first-level list is obtained from the bitstream, and if it is 0, it is determined that the first-level list is not used (i.e., in the case of no), the flag bit corresponding to the second-level list is obtained from the bitstream, and 1 is obtained, and it is determined that the second-level list is used (i.e., in the case of yes), the second-level list is determined or generated, and a candidate mode list is obtained, including [MDLM_L(CCLM), LM_CHROMA(CCLM), MDLM_T(GL-CCCM), MMLM_CHROMA(CCCM), MMLM_CHROMA(CCLM)]. The index is obtained from the bitstream, the bit string is 10, and the parsed index is 2, then according to the candidate mode list and index 2, the target prediction mode of the current block is determined to be LM_CHROMA(CCLM). Optionally, the selected prediction mode list includes a candidate prediction mode list of a current chroma component, the candidate prediction mode list of the current chroma component includes at least one hierarchical mode list, the at least one hierarchical mode list includes at least a first-level chroma prediction mode list and a second-level chroma prediction mode list, and determining a target prediction mode in the candidate prediction mode list according to an index includes: A target prediction mode in the first-level chroma prediction mode list and / or the second-level chroma prediction mode list is determined according to the index. In this embodiment, at the decoding end, the current block is predicted according to the candidate prediction mode list to determine or obtain the prediction block, and the decoding block is determined or obtained according to the prediction block, thereby improving the efficiency of video decoding. The present application also provides a processing device, please refer to FIG. 21, which is a schematic diagram of the functional modules of the processing device of the present application, which can be set in or is a processing device, and the processing device includes: The prediction module A10 is used to determine or obtain a candidate prediction mode list according to the prediction mode, predict the current block according to the candidate prediction mode list, and determine or obtain a prediction block. Optionally, predicting the current block according to the candidate prediction mode list, and determining or obtaining the predicted block includes at least one of the following: Creating or updating a candidate prediction mode list according to at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode; Determine or obtain a candidate prediction mode list of the current chrominance component according to the prediction mode of the luminance component of the neighbor block and the prediction mode of the non-neighbor block; Determine or obtain a candidate prediction mode list of the current chrominance component according to the prediction mode of the luminance component of the neighbor block, the prediction module of the non-neighbor block and the time domain prediction mode; Creating or updating a candidate prediction mode list according to a default prediction mode, a temporal prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode; Determine or obtain a candidate prediction mode list of the current chrominance component according to the luminance component prediction mode of the adjacent block and the prediction mode of the non-adjacent block; Determine or obtain a candidate prediction mode list of a current chrominance component according to a luminance component prediction mode, a non-adjacent prediction mode, and a temporal prediction mode of an adjacent block; Determine at least one prediction mode according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixels, and create or update a candidate prediction mode list according to the at least one prediction mode; Determine at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode and a time domain prediction mode according to the neighbor block pixels, and create or update a candidate prediction mode list according to the at least one prediction mode; A candidate prediction mode list is created or updated according to at least one prediction mode among a time domain prediction mode for obtaining displacement / offset of a motion vector and a prediction mode based on a historical record. Optionally, at least one prediction mode is determined according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode, and the neighbor block pixel, including at least one of the following: Calculate SAD values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode according to neighbor block pixels, and determine at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the SAD values; Calculate cost values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode according to neighbor block pixels, and determine at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the cost values; When the cost mark obtained in the bitstream is a first value, first generation values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode are calculated according to neighbor block pixels, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode is determined according to the first generation values; Obtaining a cost tag in a bitstream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, calculating a cost value of pixels of a neighboring block according to the selected first cost calculation method or the second cost calculation method, and determining at least one prediction mode among the default prediction mode, the neighboring block prediction mode, and the non-neighboring block prediction mode according to the cost value; At least two different third-generation values ​​corresponding to the default prediction mode are calculated according to the neighbor block pixels, at least two different third-generation values ​​corresponding to the neighbor block prediction mode are calculated, and at least two different third-generation values ​​corresponding to the non-neighbor block prediction mode are calculated; a fourth-generation value is determined or obtained according to the at least two different third-generation values; and at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode is determined according to the fourth-generation value. Optionally, according to at least one prediction mode, creating or updating a candidate prediction mode list includes at least one of the following: adding at least one prediction mode to a preset prediction mode list, and creating or updating a candidate prediction mode list; Sorting the determined at least one prediction mode according to a preset sequence order, and creating or updating a candidate prediction mode list; A part or all of the prediction mode lists are determined according to at least one prediction mode, and a candidate prediction mode list is created or updated according to the part or all of the prediction mode lists. Optionally, determining part or all of the prediction mode list according to at least one prediction mode includes at least one of the following: Calculating the cost of at least one prediction mode according to the calculation rule, and determining part or all of the prediction mode list according to the cost of at least one prediction mode; Inputting at least one prediction mode into a neural network to obtain an output result, and determining part or all of the prediction mode list according to the output result; Inputting at least one prediction mode into a query table for querying to obtain a query result, and determining a partial or complete prediction mode list according to the query result; Performing pattern matching according to at least one prediction pattern to determine a partial or complete prediction pattern list; At least one prediction mode is input into the mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model. Optionally, the candidate prediction mode list includes at least one of the following: List of first level patterns; Second level pattern list; List of first level chroma prediction modes; List of second level chroma prediction modes. Optionally, according to at least one prediction mode, creating or updating a candidate prediction mode list includes at least one of the following: Creating or updating a first-level pattern list according to at least one prediction mode, and determining or obtaining a second-level pattern list according to the first-level pattern list and other prediction modes outside the first-level pattern list; According to at least one prediction mode, a first-level chroma mode list is created or updated, and according to the first-level chroma mode list and other prediction modes outside the first-level chroma mode list, a second-level chroma mode list is determined or obtained. Optionally, the prediction module A10 includes: Determining at least one target prediction mode in the candidate prediction mode list; The current block is predicted according to the at least one target prediction mode to determine or obtain a prediction block. Optionally, the prediction module A10 includes: The index corresponding to the target prediction mode and the prediction block are encoded. Optionally, the prediction module A10 includes: A decoding block is determined or obtained according to the prediction block. An embodiment of the present application also provides a processing device, including a memory and a processor. A processing program is stored in the memory, and when the processing program is executed by the processor, the steps of the processing method in any of the above embodiments are implemented. An embodiment of the present application further provides a storage medium, on which a processing program is stored. When the processing program is executed by a processor, the steps of the processing method in any of the above embodiments are implemented. In the embodiments of the processing device, processing equipment and storage medium provided in the present application, all technical features of any of the above-mentioned processing method embodiments may be included, and the expansion and explanation content of the specification are basically the same as those of the embodiments of the above-mentioned method, and will not be repeated here. The embodiment of the present application further provides a computer program product, which includes a computer program code. When the computer program code runs on a computer, the computer executes the methods in the above various possible implementation modes. An embodiment of the present application also provides a chip, including a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that a device equipped with the chip executes the methods in various possible implementation modes as described above. It is understood that the above scenarios are only examples and do not constitute a limitation on the application scenarios of the technical solutions provided in the embodiments of the present application. The technical solutions of the present application can also be applied to other scenarios. For example, it is known to those skilled in the art that with the evolution of the system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems. The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments. The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs. In the present application, the same or similar terminology concepts, technical solutions and / or application scenario descriptions are generally described in detail only the first time they appear. When they appear again later, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of the present application, for the same or similar terminology concepts, technical solutions and / or application scenario descriptions that are not described in detail later, reference can be made to the previous related detailed descriptions. In the present application, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. The various technical features of the technical solution of the present application can be arbitrarily combined. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application. Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present application. In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a storage medium, or transmitted from one storage medium to another storage medium. For example, computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. Available media can be magnetic media, (e.g., floppy disk, storage disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state storage disk Solid State Disk (SSD)), etc. The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A processing method, wherein: Includes steps: S10: Determine or obtain a candidate prediction mode list according to the prediction mode; S20: Predict the current block according to the candidate prediction mode list to determine or obtain a predicted block.

2. The processing method according to claim 1, wherein: The step S10 includes at least one of the following: Creating or updating a candidate prediction mode list according to at least one of a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode, or a time domain prediction mode; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the neighbor block and the cross-component prediction mode of the non-neighbor block; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the neighbor block, the cross-component prediction mode of the non-neighbor block, and the time-domain prediction mode; Determine or obtain a candidate prediction mode list of the current chrominance component according to the prediction mode of the luminance component of the neighbor block and the prediction mode of the non-neighbor block; Determine or obtain a candidate prediction mode list of the current chrominance component according to the prediction mode of the luminance component of the neighbor block, the prediction module of the non-neighbor block and the time domain prediction mode; Creating or updating a candidate prediction mode list according to a default prediction mode, a temporal prediction mode, an adjacent block prediction mode and / or a non-adjacent block prediction mode; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the adjacent block and the cross-component prediction mode of the non-adjacent block; Determine or obtain a candidate prediction mode list according to the cross-component prediction mode of the adjacent block, the cross-component prediction mode of the non-adjacent block, and the time-domain prediction mode; Determine or obtain a candidate prediction mode list of the current chrominance component according to the luminance component prediction mode of the adjacent block and the prediction mode of the non-adjacent block; Determine or obtain a candidate prediction mode list of the current chrominance component according to the luminance component prediction mode of the adjacent block, the non-adjacent block prediction mode and the time domain prediction mode; Determine at least one prediction mode according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixels, and create or update a candidate prediction mode list according to the at least one prediction mode; Determine at least one prediction mode among a default prediction mode, a neighbor block prediction mode, a non-neighbor block prediction mode and a time domain prediction mode according to the neighbor block pixels, and create or update a candidate prediction mode list according to the at least one prediction mode; A candidate prediction mode list is created or updated according to at least one prediction mode among a time domain prediction mode for obtaining displacement / offset of a motion vector and a prediction mode based on a historical record.

3. The processing method according to claim 2, wherein: The determining at least one prediction mode according to the default prediction mode, the neighbor block prediction mode, the non-neighbor block prediction mode and the neighbor block pixel includes at least one of the following: Calculate SAD values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode according to neighbor block pixels, and determine at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the SAD values; Calculate cost values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode according to neighbor block pixels, and determine at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode according to the cost values; When the cost mark obtained in the bitstream is a first value, first generation values ​​of a default prediction mode, a neighbor block prediction mode, and a non-neighbor block prediction mode are calculated according to neighbor block pixels, and at least one prediction mode among the default prediction mode, the neighbor block prediction mode, and the non-neighbor block prediction mode is determined according to the first generation values; Obtaining a cost tag in a bitstream, selecting a first cost calculation method when the cost tag is a first value, and selecting a second cost calculation method when the cost tag is a second value, calculating a cost value of pixels of a neighboring block according to the selected first cost calculation method or the second cost calculation method, and determining at least one prediction mode among the default prediction mode, the neighboring block prediction mode, and the non-neighboring block prediction mode according to the cost value; At least two different third-generation values ​​corresponding to the default prediction mode are calculated according to the neighbor block pixels, at least two different third-generation values ​​corresponding to the neighbor block prediction mode are calculated, and at least two different third-generation values ​​corresponding to the non-neighbor block prediction mode are calculated; a fourth-generation value is determined or obtained according to the at least two different third-generation values; and at least one prediction mode among the default prediction mode, the neighbor block prediction mode and the non-neighbor block prediction mode is determined according to the fourth-generation value.

4. The processing method according to claim 2, wherein: The creating or updating the candidate prediction mode list according to at least one prediction mode includes at least one of the following: adding at least one prediction mode to a preset prediction mode list, and creating or updating a candidate prediction mode list; Sorting the determined at least one prediction mode according to a preset sequence order, and creating or updating a candidate prediction mode list; A part or all of the prediction mode lists are determined according to at least one prediction mode, and a candidate prediction mode list is created or updated according to the part or all of the prediction mode lists.

5. The processing method according to claim 4, wherein: The determining of part or all of the prediction mode list according to at least one prediction mode includes at least one of the following: Calculating the cost of at least one prediction mode according to the calculation rule, and determining part or all of the prediction mode list according to the cost of at least one prediction mode; Inputting at least one prediction mode into a neural network to obtain an output result, and determining part or all of the prediction mode list according to the output result; Inputting at least one prediction mode into a query table for querying to obtain a query result, and determining a partial or complete prediction mode list according to the query result; Performing pattern matching according to at least one prediction pattern to determine a partial or complete prediction pattern list; At least one prediction mode is input into the mathematical model, and part or all of the prediction mode list is determined according to the output result of the mathematical model.

6. The processing method according to claim 2, wherein: The candidate prediction mode list includes at least one of the following: List of first level patterns; Second level pattern list; List of first level chroma prediction modes; List of second level chroma prediction modes.

7. The processing method according to claim 6, wherein: The creating or updating the candidate prediction mode list according to at least one prediction mode includes at least one of the following: Creating or updating a first-level pattern list according to at least one prediction mode, and determining or obtaining a second-level pattern list according to the first-level pattern list and other prediction modes outside the first-level pattern list; According to at least one prediction mode, a first-level chroma mode list is created or updated, and according to the first-level chroma mode list and other prediction modes outside the first-level chroma mode list, a second-level chroma mode list is determined or obtained.

8. The processing method according to any one of claims 1 to 7, wherein: The step S20 comprises: Determining at least one target prediction mode in the candidate prediction mode list; The current block is predicted according to the at least one target prediction mode to determine or obtain a prediction block.

9. The processing method according to claim 8, wherein: The method further comprises: The index corresponding to the target prediction mode and the prediction block are encoded.

10. The processing method according to any one of claims 1 to 7, wherein: The method further comprises: A decoding block is determined or obtained according to the prediction block.

11. A processing device, wherein: include: A memory and a processor, wherein a processing program is stored in the memory, and when the processing program is executed by the processor, the steps of the processing method according to claim 1 are implemented.

12. A storage medium, wherein: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the processing method according to claim 1 are implemented.

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