WO2023216866A1 - 图像处理方法、智能终端及存储介质 - Google Patents

图像处理方法、智能终端及存储介质 Download PDF

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Publication number
WO2023216866A1
WO2023216866A1 PCT/CN2023/090367 CN2023090367W WO2023216866A1 WO 2023216866 A1 WO2023216866 A1 WO 2023216866A1 CN 2023090367 W CN2023090367 W CN 2023090367W WO 2023216866 A1 WO2023216866 A1 WO 2023216866A1
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Prior art keywords
sampling point
reference sampling
partition
image block
target image
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PCT/CN2023/090367
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English (en)
French (fr)
Inventor
刘雨田
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Shenzhen Transsion Holdings Co Ltd
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Shenzhen Transsion Holdings Co Ltd
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Publication of WO2023216866A1 publication Critical patent/WO2023216866A1/zh
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/103—Selection of coding mode or of prediction mode

Definitions

  • This application relates to the field of image processing technology, and specifically relates to an image processing method, an intelligent terminal and a storage medium.
  • prediction mode In the process of video encoding and decoding, using prediction mode to predict image blocks is a very important step. Predicting image blocks through corresponding prediction modes can effectively remove redundancy in the video temporal or spatial domain, thereby compressing the video for better transmission.
  • this application provides an image processing method, which can be applied to smart terminals, including:
  • the prediction result set of the target image block is determined according to the preset prediction mode, and the prediction result set is used to determine the prediction result of the target image block.
  • the target image block includes a first partition and/or a second partition, and the first partition and/or the second partition are image areas divided by dividing lines;
  • the preset prediction mode includes the The prediction mode used by the partitions divided by the dividing lines in the target image block;
  • the prediction result set includes a first prediction result set and/or a second prediction result set.
  • the method further includes: determining a target division mode parameter of the target image block according to the first division mode set; the target division mode parameter includes a prediction indicating a prediction mode used by the corresponding partition in the target image block. Mode indication information.
  • determining the prediction result set of the corresponding partition in the target image block according to the preset prediction mode includes at least one of the following: if the prediction mode is the first prediction mode, determining the prediction result set according to the corresponding partition in the target image block.
  • the motion vector determines the prediction result set of the corresponding partition in the target image block; if the prediction mode is the second prediction mode, determine the prediction result set of the corresponding partition in the target image block according to the target reference sampling point of the corresponding partition in the target image block.
  • the set of prediction results corresponding to the partition includes at least one of the following: if the prediction mode is the first prediction mode, determining the prediction result set according to the corresponding partition in the target image block.
  • the motion vector determines the prediction result set of the corresponding partition in the target image block; if the prediction mode is the second prediction mode, determine the prediction result set of the corresponding partition in the target image block according to the target reference sampling point of the corresponding partition in the target image block.
  • determining the prediction result set of the corresponding partition in the target image block based on the motion vector of the corresponding partition in the target image block includes: based on the merge candidate list and/or target reference sampling of the target image block Determine the first motion vector and/or the second motion vector of the corresponding partition in the target image block; determine the prediction of the corresponding partition in the target image block based on the first motion vector and/or the second motion vector. Result collection.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point.
  • the method also includes at least one of the following:
  • the first reference sampling point and the second reference sampling point are different;
  • the first reference sampling point is adjacent to the corresponding partition and not adjacent to another partition
  • the second reference sampling point is not adjacent to the corresponding partition and adjacent to the other partition;
  • the positional relationship of the first reference sampling point relative to the corresponding partition is different from the positional relationship of the second reference sampling point relative to the corresponding partition;
  • the first reference sampling point or the second reference sampling point is a pixel point adjacent to the coding tree unit where the target image block is located and not adjacent to the target image block.
  • the second prediction mode includes at least one type of second prediction mode. If the prediction mode is the second prediction mode, the method further includes: selecting from the at least one type of second prediction mode. In the mode, a second prediction mode of the target type used in the corresponding partition of the target image block is determined, and the second prediction mode of the target type is used to determine a prediction result set of the target image block.
  • determining the prediction result set of the corresponding partition in the target image block based on the target reference sampling point of the corresponding partition in the target image block includes the following steps: S21: Determine the prediction result set of the corresponding partition in the target image block. The first reference sampling point and/or the second reference sampling point; S22: Determine the prediction result set of the corresponding partition in the target image block according to the first reference sampling point and/or the second reference sampling point.
  • the step S21 includes: determining the target according to the positional relationship between the corresponding partition in the target image block and the boundary sampling point of the dividing line used by the target image block in the target image block.
  • the method further includes: determining a boundary passed by a dividing line used by the target image block according to a boundary mapping table; and determining the boundary sampling point according to a boundary passed by the dividing line.
  • determining the boundary sampling point according to the boundary passed by the dividing line includes at least one of the following: when the dividing line used by the target image block passes the first boundary of the target image block, It is determined that the boundary sampling point includes a first boundary sampling point; when the dividing line used by the target image block passes through the second boundary of the target image block, it is determined that the boundary sampling point includes a second boundary sampling point.
  • the method for determining the boundary sampling point includes: determining a dividing line equation according to a target division mode parameter of the target image block; and determining at least one boundary sampling point according to a boundary reference point and the dividing line equation.
  • the corresponding partition in the target image block is determined based on the positional relationship between the corresponding partition in the target image block and the boundary sampling point in the target image block of the dividing line used by the target image block.
  • the first reference sampling point and/or the second reference sampling point of the partition include: determining the first coordinate range and/or the second coordinate range according to the at least one boundary sampling point; and determining the first coordinate range and/or the second coordinate range according to the first coordinate range and/or the The second coordinate range determines the first reference sampling point and/or the second reference sampling point.
  • the step S21 includes: determining the partition range of the corresponding partition in the target image block based on the distance between the sampling point of the corresponding partition in the target image block and the dividing line; based on the coordinates of the first sampling point
  • the range and the partition range determine the first reference sampling point and/or the first reference sampling point of the corresponding partition in the target image block.
  • the step S22 includes: filling the second reference sampling point according to the first reference sampling point to obtain a filled reference sampling point; and filling the second reference sampling point according to the first reference sampling point and the filled reference sampling point.
  • the reference sampling points determine the prediction result set of the corresponding partition of the target image block.
  • filling the second reference sampling point according to the first reference sampling point to obtain a filled reference sampling point includes: determining at least one filling reference sample in the first reference sampling point point; determine the filling value of the second reference sampling point based on the sampling value of the at least one filling reference sampling point; fill the second reference sampling point based on the filling value to obtain a filled reference sampling point.
  • determining the filling value of the second reference sampling point based on the sampling value of the at least one filling reference sampling point includes: based on the position between each filling reference sampling point and the second reference sampling point The filling weight of each filling reference sampling point is determined based on the relationship; the filling value of the second reference sampling point is determined based on the filling weight and the sampling value of each filling reference sampling point.
  • the position relationship includes the distance between each filling reference sampling point and the second reference sampling point;
  • the first reference sampling point includes the distance between the first reference sampling point and the target image block.
  • the at least one filling reference sampling point includes a first reference sampling point adjacent to the first boundary. a first filling reference sampling point among the adjacent first reference sampling points, and/or a second filling reference sampling point among the first reference sampling points adjacent to the second boundary.
  • determining the filling value of the second reference sampling point based on the filling weight and the sampling value of each filling reference sampling point includes: based on the sampling value of the first filling reference sampling point and the first filling The filling weight of the reference sampling point, and/or, the sampling value of the second filling reference sampling point and the filling weight of the second filling reference sampling point, determine the filling value of the second reference sampling point adjacent to the corresponding boundary.
  • This application provides another image processing method that can be applied to smart terminals, including the following steps:
  • the target reference sampling point is determined based on the positional relationship between the reference sampling point and the sampling point in the image block partition.
  • step S1 includes: determining the target reference sampling point through the positional relationship between the reference sampling point and the sampling point in the image block partition.
  • determining the target reference sampling point through the positional relationship between the reference sampling point and the sampling point in the image block partition includes: determining at least one boundary sampling point from the sampling points in the image block partition; based on the reference sampling point and the The positional relationship between at least one boundary sampling point determines the target reference sampling point.
  • the step S2 includes: determining a preset prediction mode according to the prediction mode indication information of the image block; and determining a prediction result set according to the preset prediction mode and the target reference sampling point.
  • the preset prediction mode is at least one of the following: a prediction mode used by partitions divided by dividing lines in the image block and/or a prediction mode used by adjacent image blocks.
  • the preset prediction mode is a prediction mode used by partitions divided by dividing lines in the image block; and determining the prediction result set according to the preset prediction mode and the target reference sampling point includes: if If the prediction mode includes the first prediction mode and/or the second prediction mode, then according to the motion vector and/or the target reference sampling point, the prediction result set of the corresponding partition of the image block is determined; according to the prediction result set of the corresponding partition of the image block A set of prediction results for the image block is determined.
  • determining a prediction result set of the corresponding partition of the image block according to the target reference sampling point including:
  • the prediction mode includes a second prediction mode, fill the second reference sampling point according to the first reference sampling point to obtain a filled reference sampling point;
  • a prediction result set corresponding to the partition of the image block is determined according to the first reference sampling point and the filled reference sampling point.
  • the present application also provides an intelligent terminal, including: a memory and a processor, wherein an image processing program is stored on the memory, and when the image processing program is executed by the processor, the steps of any of the above image processing methods are implemented.
  • This application also provides a computer storage medium.
  • the computer storage medium stores a computer program.
  • the computer program is executed by a processor, the steps of any of the above image processing methods are implemented.
  • the image processing method of the present application can be applied to smart terminals, including: determining a prediction result set of a target image block according to a preset prediction mode, and the prediction result set is used to determine the prediction result of the target image block.
  • the preset prediction mode can be any type of preset prediction mode.
  • the selection range of the preset prediction mode is not limited, and the use flexibility is high; a more accurate target image block can be determined according to the preset prediction mode.
  • the prediction result set corresponding to the partition in the prediction result set can then determine the prediction result of the target image block with a smaller deviation based on the prediction result set, thereby improving the accuracy of the prediction result. It can be seen that using this solution can realize the function of flexibly using the preset prediction mode, improving the accuracy of the prediction results, and solving the problem of imbalance between the flexibility of using the prediction mode and the accuracy of the prediction results.
  • Figure 1 is a schematic diagram of the hardware structure of a mobile terminal that implements various embodiments of the present application
  • FIG. 2 is a communication network system architecture diagram provided by an embodiment of the present application.
  • Figure 3 is a schematic flowchart of an image processing method according to the first embodiment
  • Figure 4a is a schematic diagram showing the effect of dividing image blocks by a dividing line according to the first embodiment
  • Figure 4b is a schematic diagram of angle division according to the first embodiment
  • Figure 4c is a schematic diagram showing various offsets corresponding to angle ⁇ i according to the first embodiment
  • Figure 4d is a schematic diagram of a division mode according to the first embodiment
  • Figure 5 is a schematic flowchart of an image processing method according to a second embodiment
  • Figure 6b is a schematic diagram illustrating an exemplary merging of candidate lists according to the second embodiment
  • Figure 6c is a schematic diagram of an exemplary target reference sampling point according to the second embodiment
  • Figure 6d is a schematic diagram of encoding of a target image block according to the second embodiment
  • Figure 7 is a schematic flowchart of an image processing method according to a third embodiment
  • Figure 8a shows a situation in which the dividing line in the target image block passes through the boundary according to the third embodiment
  • Figure 8b is a schematic diagram of the positional relationship between the boundary reference point and the boundary sampling point according to the third embodiment
  • Figure 8c is a schematic diagram of a target reference sampling point division according to the third embodiment.
  • Figures 9a and 9b are schematic diagrams of some filled reference sampling points according to a third embodiment
  • Figure 9c is a schematic diagram showing the use of filling reference sampling points according to the third embodiment.
  • Figure 9d is a schematic diagram illustrating another method of using filled reference sampling points according to the third embodiment.
  • Figure 9e is a schematic diagram illustrating yet another use of filling reference sampling points according to the third embodiment.
  • Figure 9f is a schematic diagram of a wide-angle intra prediction mode according to the third embodiment.
  • Figure 10 is a schematic flowchart of an image processing method according to a fourth embodiment
  • FIG 11 is a schematic flowchart of an image processing method according to the fifth embodiment.
  • Figure 12 is a schematic structural diagram of an image processing device according to an embodiment of the present application.
  • Figure 13 is a schematic diagram of the hardware structure of a controller according to an embodiment of the present application.
  • Figure 14 is a schematic diagram of the hardware structure of a network node according to an embodiment of the present application.
  • first, second, third, etc. may be used herein to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other.
  • first information may also be called second information, and similarly, the second information may also be called first information.
  • word “if” as used herein may be interpreted as “when” or “when” or “in response to determining.”
  • singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context indicates otherwise.
  • 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"; another example is, “ 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". Exceptions to this definition occur only when the combination of elements, functions, steps, or operations is inherently mutually exclusive in some manner.
  • each step in the flow chart in the embodiment of the present application is displayed in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this article, the execution of these steps is not strictly limited in order, and they can be executed in other orders. Moreover, at least some of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and their execution order is not necessarily sequential. may be performed in turn or alternately with other steps or sub-steps of other steps or at least part of stages.
  • the words “if” or “if” as used herein may be interpreted as “when” or “when” or “in response to determination” or “in response to detection.”
  • the phrase “if determined” or “if (stated condition or event) is detected” may be interpreted as “when determined” or “in response to determining” or “when (stated condition or event) is detected )” or “in response to detecting (a stated condition or event)”.
  • step codes such as S501 and S502 are used for the purpose of describing the corresponding content more clearly and concisely, and do not constitute a substantial restriction on the sequence. Those skilled in the art may S502 will be executed first and then S501, etc., but these should be within the protection scope of this application.
  • Smart terminals can be implemented in various forms.
  • the smart terminals described in this application may include mobile phones, tablet computers, notebook computers, PDAs, personal digital assistants (Personal Digital Assistant, PDA), portable media players (Portable Media Player, PMP), navigation devices, Smart terminals such as wearable devices, smart bracelets, and pedometers, as well as fixed terminals such as digital TVs and desktop computers.
  • PDA Personal Digital Assistant
  • PMP portable media players
  • navigation devices Smart terminals such as wearable devices, smart bracelets, and pedometers
  • Smart terminals such as wearable devices, smart bracelets, and pedometers
  • fixed terminals such as digital TVs and desktop computers.
  • a mobile terminal will be taken as an example.
  • the structure according to the embodiments of the present application can also be applied to fixed-type terminals.
  • the mobile terminal 100 may include: an RF (Radio Frequency, radio frequency) unit 101, a WiFi module 102, an audio output unit 103, and a /V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111 and other components.
  • RF Radio Frequency, radio frequency
  • the above wireless communication can use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication, Global Mobile Communication System), GPRS (General Packet Radio Service, General Packet Radio Service), CDMA2000 (Code Division Multiple Access 2000) , Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access, wideband code division multiple access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access, time division synchronous code division multiple access), FDD-LTE (Frequency Division Duplexing-Long Term Evolution, frequency division duplex long-term evolution) ), TDD-LTE (Time Division Duplexing-Long Term Evolution, Time Division Duplexing-Long Term Evolution) and 5G, etc.
  • GSM Global System of Mobile communication, Global Mobile Communication System
  • GPRS General Packet Radio Service, General Packet Radio Service
  • CDMA2000 Code Division Multiple Access 2000
  • Code Division Multiple Access 2000 Code Division Multiple Access 2000
  • WCDMA Wideband Code Division Multiple Access, wideband code division multiple
  • WiFi is a short-distance wireless transmission technology.
  • the mobile terminal can help users send and receive emails, browse web pages, access streaming media, etc. through the WiFi module 102. It provides users with wireless broadband Internet access.
  • FIG. 1 shows the WiFi module 102, it can be understood 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 may, when the mobile terminal 100 is in a call signal receiving mode, a call mode, a recording mode, a voice recognition mode, a broadcast receiving mode, etc., receive the audio signal received by the radio frequency unit 101 or the WiFi module 102 or store it in the memory 109 The audio data is converted into audio signals and output as sound. Furthermore, the audio output unit 103 may also provide audio output related to a specific function performed by the mobile terminal 100 (eg, call signal reception sound, message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, or the like.
  • the A/V input unit 104 is used to receive audio or video signals.
  • the A/V input unit 104 may include a graphics processor (Graphics Processing Unit, GPU) 1041 and a microphone 1042.
  • the graphics processor 1041 can process still pictures or images obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. Video image data is processed.
  • the processed image frames may be displayed on the display unit 106.
  • the image frames processed by the graphics processor 1041 may be stored in the memory 109 (or other storage media) or sent via the radio frequency unit 101 or WiFi module 102.
  • the microphone 1042 can receive sounds (audio data) via the microphone 1042 in operating modes such as a phone call mode, a recording mode, a voice recognition mode, and the like, and can process such sounds into audio data.
  • the processed audio (voice) data can be converted into a format that can be sent to a mobile communication base station via the radio frequency unit 101 for output in a phone call mode.
  • Microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to eliminate (or suppress) noise or interference generated in the process of receiving and transmitting audio signals.
  • the mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors.
  • the light sensor includes an ambient light sensor and a proximity sensor.
  • the ambient light sensor can adjust the brightness of the display panel 1061 according to the brightness of the ambient light.
  • the proximity sensor can turn off the display when the mobile terminal 100 moves to the ear. Panel 1061 and/or backlight.
  • the accelerometer sensor can detect the magnitude of acceleration in various directions (usually three axes). It can detect the magnitude and direction of gravity when stationary.
  • It can be used to identify applications of mobile phone posture (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for the mobile phone, it can also be configured with fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, Other sensors such as thermometers and infrared sensors will not be described in detail 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.
  • LCD liquid crystal display
  • OLED organic light-emitting diode
  • the user input unit 107 may be used to receive input numeric or character information, and generate key signal input related to user settings and function control of the mobile terminal.
  • 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 operations on or near the touch panel 1071 (for example, the user uses a finger, stylus, or any suitable object or accessory on or near the touch panel 1071 operation), and drive the corresponding connection device according to the preset program.
  • the touch panel 1071 may include two parts: a touch detection device and a touch controller.
  • 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 and converts it into contact point coordinates , and then sent to the processor 110, and can receive the commands sent by the processor 110 and execute them.
  • the touch panel 1071 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave.
  • the user input unit 107 may also include other input devices 1072.
  • other input devices 1072 may include but are not limited to one or more of physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, joysticks, etc., which are not specifically discussed here. limited.
  • the touch panel 1071 can cover the display panel 1061.
  • 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 determines the type of the touch event according to the touch event.
  • the type provides corresponding visual output on the display panel 1061.
  • 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. The implementation of the input and output functions of the mobile terminal is not limited here.
  • the interface unit 108 serves as an interface through which at least one external device can be connected to the mobile terminal 100 .
  • external devices may include a wired or wireless headphone port, an external power (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, audio input/output (I/O) port, video I/O port, headphone port, etc.
  • the interface unit 108 may be used to receive input (eg, 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 connect between the mobile terminal 100 and an external device. Transfer data between devices.
  • Memory 109 may be used to store software programs as well as various data.
  • the memory 109 may mainly include a storage program area and a storage data area.
  • the storage program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.;
  • the storage data area may Store data created based on the use of the mobile phone (such as audio data, phone book, etc.), etc.
  • memory 109 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • the processor 110 is the control center of the mobile terminal, using various interfaces and lines to connect various parts of the entire mobile terminal, by running or executing software programs and/or modules stored in the memory 109, and calling data stored in the memory 109 , execute various functions of the mobile terminal and process data, thereby overall monitoring the mobile terminal.
  • the processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor and a modem processor.
  • the application processor mainly processes the operating system, user interface, application programs, etc., and modulation
  • the demodulation processor mainly handles wireless communications. It can be understood that the above 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) that supplies power to various components.
  • a power supply 111 such as a battery
  • the power supply 111 may be logically connected to the processor 110 through a power management system, thereby managing charging, discharging, and power consumption management through the power management system. and other functions.
  • the mobile terminal 100 may also include a Bluetooth module, etc., which will not be described again here.
  • FIG. 2 is an architecture diagram of a communication network system provided by an embodiment of the present application.
  • the communication network system is an LTE system of universal mobile communication technology.
  • the LTE system includes UEs (User Equipment, User Equipment) connected in sequence. )201, E-UTRAN (Evolved UMTS Terrestrial Radio Access Network, Evolved UMTS Terrestrial Radio Access Network) 202, EPC (Evolved Packet Core, Evolved Packet Core Network) 203 and the operator's IP business 204.
  • UEs User Equipment, User Equipment
  • E-UTRAN Evolved UMTS Terrestrial Radio Access Network
  • EPC Evolved Packet Core, Evolved Packet Core Network
  • UE201 may be the above-mentioned mobile terminal 100, which will not be described again here.
  • E-UTRAN202 includes eNodeB2021 and other eNodeB2022, etc.
  • eNodeB2021 can be connected to other eNodeB2022 through backhaul (for example, X2 interface), eNodeB2021 is connected to EPC203, and eNodeB2021 can provide access from UE201 to EPC203.
  • backhaul for example, X2 interface
  • EPC 203 may include MME (Mobility Management Entity, mobility management entity) 2031, HSS (Home Subscriber Server, home user server) 2032, other MME 2033, SGW (Serving Gate Way, service gateway) 2034, PGW (PDN Gate Way, packet data Network Gateway) 2035 and PCRF (Policy and Charging Rules Function, policy and charging functional entity) 2036, etc.
  • 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 the home location register (not shown in the figure), and to save some user-specific information about service characteristics, data rates, etc. All user data can be sent through SGW2034.
  • PGW2035 can provide IP address allocation and other functions for UE201.
  • PCRF2036 is the policy and charging control policy decision point for business data flows and IP bearer resources. It is a policy and charging execution functional unit (Not shown) Select and provide available policy and billing control decisions.
  • IP services 204 may include the Internet, Intranet, IMS (IP Multimedia Subsystem, IP Multimedia Subsystem) or other IP services.
  • IMS IP Multimedia Subsystem, IP Multimedia Subsystem
  • predicting image blocks is an essential step. By predicting image blocks to obtain prediction blocks, and constructing residual blocks with smaller energy, transmission bits can be reduced. Prediction of image blocks can be achieved through some preset prediction modes, which can include inter prediction modes and intra prediction modes.
  • Inter-frame prediction mode uses the correlation between pixels of different images to remove temporal redundancy. Compared with intra-frame prediction mode, it is a prediction mode with higher coding efficiency.
  • Intra-frame prediction mode uses the correlation of the video spatial domain to predict the current pixel using adjacent coded pixels in the same frame.
  • the current coding unit (CU) can use reconstructed pixels at adjacent positions to predict pixels in the current CU.
  • the intra prediction mode can be: Direct Current (DC) mode, or planar (PLANAR) mode, or angle mode.
  • the image can be divided into different areas for prediction.
  • the geometric partitioning mode (GPM) is also proposed.
  • GPM mode can more precisely divide the boundaries of moving objects in the image, and divide the edge coding unit (Coding Unit) of the moving object into rectangular or non-rectangular sub-coding by fitting the dividing line with the boundary of the moving object.
  • the coding unit is predicted to obtain the prediction value of the entire coding unit.
  • Figure 3 is a schematic flow chart of an image processing method according to the first embodiment.
  • the execution subject in this embodiment can be a computer device or a cluster composed of multiple computer devices.
  • the computer device can It may be an intelligent terminal (such as the aforementioned mobile terminal 100), or it may be a server.
  • the execution subject in this embodiment is an intelligent terminal as an example for explanation.
  • the target image block refers to the image block currently being encoded in the input video image (ie, the video frame), which may be referred to as the current block, the current image block, or the current coding block.
  • the target image block can be a coding tree unit (Coding Tree Units, CTU) in the input video image, or a coding unit (Coding Unit, CU), or a transformation unit (Transform Unit, TU), etc. There are no restrictions on this.
  • the target image block can be a square block (that is, the size of the image block is square) or a non-square block. Non-square blocks can be rectangular-sized image blocks, including horizontal blocks (width is greater than height) and/or vertical blocks (height is greater than width). For example, when the target image block is a CU, the CU can be a square block or a non-square block piece. There are no restrictions here.
  • the target image block includes a first partition and/or a second partition, and the first partition and/or the second partition are image areas divided by dividing lines.
  • the corresponding partition in the target image block may be the first partition or the second partition.
  • the first partition and/or the second partition are rectangular or non-rectangular areas in the target image block, and the first partition and the second partition are relative.
  • the first partition and the second partition are rectangular, triangular, or trapezoidal areas related to the target image block obtained through the GPM mode.
  • a horizontal dividing line is used to divide the target image block.
  • the image area above the horizontal dividing line can be called the first partition
  • the image area below the horizontal dividing line can be called the first partition.
  • the preset prediction mode includes a prediction mode used by partitions divided by dividing lines in the target image block.
  • the partitions divided by the dividing line in the target image block include the first partition and/or the second partition.
  • the preset prediction mode includes the prediction mode used by the first partition and/or the prediction used by the second partition in the target image block. model.
  • the prediction mode used by the adjacent image block may be used as the prediction mode used by the partition divided by the dividing line in the target image block.
  • the prediction mode that is used the most or more frequently among the prediction modes used by at least one adjacent image block may be used as the prediction mode used by the partitions divided by the dividing lines in the target image.
  • the prediction mode used by the partitions divided by the dividing lines in the target image can be obtained through other methods.
  • the adjacent image blocks are coded image blocks adjacent to the target image block, and the adjacent image blocks may include one or more. When the adjacent image block includes one, the prediction mode used by the adjacent image block can be directly determined as the preset prediction mode.
  • the preset prediction mode may also be determined by calculating the rate distortion cost. Due to the correlation between adjacent image blocks and the target image block, by referring to the prediction modes used by adjacent image blocks, the preset prediction mode used by the current encoding block can not only ensure encoding quality, but also improve encoding efficiency.
  • the prediction mode used for the partitions divided by the dividing lines in the target image block may be determined using a rate-distortion cost.
  • the preset prediction mode is a prediction mode that better matches the corresponding partition in the target image block, which can effectively ensure the accuracy of the prediction results. Compared with the first two methods, the accuracy will be improved.
  • the preset prediction modes corresponding to different partitions may be different. For example, the prediction modes used by the first partition and the second partition in the target image block are different.
  • the determination process for the preset prediction mode includes the following 1) and 2):
  • the rate distortion cost of each division mode included in the first division mode applied to the target image block can be determined, and then the division mode with the smallest rate distortion cost is determined as the division line in the target image block.
  • the target partitioning mode used by the partitioned partition That is to say, all division modes included in the first division mode can be traversed to determine the rate distortion cost required for the target image block to adopt various division modes respectively. In order to achieve optimal coding performance, it is necessary to seek as much as possible under a certain code rate.
  • the rate distortion costs corresponding to each division mode can be compared, and the division mode with the minimum rate distortion cost can be determined and the rate distortion cost can be minimized.
  • the division mode is used as the target division mode of the target image block. This target division mode is used to divide the target image block into different partitions, thereby achieving optimal encoding of the target image block.
  • the mode parameters corresponding to the target division mode can be queried from the first division mode parameter set, and the mode parameters are used as the target division mode parameters to be used for the target image blocks. Subsequent divisions can be performed according to the target division mode parameters.
  • the target image block is predicted and the prediction result of the target image block is obtained.
  • the first division mode is the GPM mode
  • the division mode is any one of the 64 division modes corresponding to the GPM mode
  • the first division mode parameter set is the GPM mapping table
  • the target division mode parameter is the GPM parameter.
  • the encoder determines the color component of the current block (including luminance component and/or chrominance component); based on the parameters of the current block, multiple prediction modes are used (including intra prediction mode and/or (or inter-frame prediction mode) perform predictive coding on color components respectively, calculate the rate distortion cost corresponding to each prediction mode, and determine the minimum rate distortion cost from the rate distortion costs corresponding to multiple prediction modes.
  • the prediction mode corresponding to the minimum rate distortion cost is determined as the prediction mode parameter of the current block.
  • the prediction mode corresponding to the minimum rate distortion cost is the GPM mode
  • the GPM mode is determined as the prediction mode parameter of the current block.
  • the prediction mode parameters corresponding to the GPM mode are binarized and packaged into a bit stream for transmission.
  • the division mode with the minimum rate distortion cost can be determined, and the division mode with the minimum rate distortion cost is used as the target division mode of the current block.
  • the target partition mode use the GPM partition index gpm_partition_idx, angle index angleIdx and distance
  • the mapping table of the distance index distanceIdx determines the target GPM partition index merge_gpm_partition_idxT, the target angle index angleIdxT and the target distance index distanceIdxT corresponding to the target partition mode.
  • the GPM mapping table is shown in Table 1 below.
  • the GPM mode includes 64 division modes, and each division mode corresponds to a dividing line.
  • GPM parameters include partition index gpm_partition_idx, angle index angleIdx and distance index distanceIdx. It should be noted that when the angle index angleIdx takes different values, it corresponds to the different angles shown in Figure 4b Optionally, i ranges from 1 to 24. When the distance index distanceIdx takes different values, it corresponds to ⁇ j in Figure 4c. Optionally, j is 0 to 3. Because the angle index angleIdx, the combination of different values of the distance index distanceIdx respectively constitute different division methods. For example, as shown in Figure 4d.
  • a lookup table can be used to represent the relationship between the partition index pm_partition_idx, the angle index angleIdx, and the distance index distanceIdx. For example, see Table 2 below.
  • angle index angleIdx corresponds to the sine of the angle and the distance index distanceIdx corresponds to ⁇ j.
  • you can set the angleIdx and The mapping table is shown in Table 3 below. In another implementation, it is also possible to set an intermediate variable about angleIdx and mapping table.
  • a mapping table regarding the angle index angleIdx and the slope may also be set. For example, use a variety of fixed slopes ⁇ slope0, slope1, slope2,..., slopen ⁇ to construct a table of angles at unequal intervals.
  • the prediction mode parameters of the target image block can be determined at the decoding end by parsing the bit stream.
  • the prediction mode parameters are parameter information used to indicate the prediction mode used by the target image block. . If the prediction mode parameter is a target division mode parameter used to indicate the use of the first division mode, then the specific type of first division mode to be used may be determined by the target division mode parameter.
  • the target partition mode parameter includes at least one of a segmentation index, an angle index, and a distance index.
  • the target partition mode parameter includes a prediction mode used to indicate a prediction mode used by a corresponding partition in the target image block. Instructions.
  • the prediction mode indication information is used at the decoding end to indicate the prediction mode used by the corresponding partition in the target image block.
  • the prediction mode used by the partition included in the target image block may be determined according to the prediction mode indication information included in the target partition mode parameter.
  • the prediction mode indication information includes prediction mode indication information used to indicate the prediction mode used by the first partition, and/or prediction mode indication information used to indicate the prediction mode used by the second partition.
  • the prediction mode indication information may be a tag or index of the prediction mode type used by the corresponding partition of the target image block at the encoding end.
  • the prediction mode includes an intra prediction mode and/or an inter prediction mode. If the preset prediction mode is intra prediction mode, you can use the coded pixels of the corresponding partition in the target image block to predict the pixels in the current image block (such as angle prediction mode), or you can use the coded brightness component to predict the current image.
  • the color component of the block for example in CCLM (Crossed Component Linear Model) mode, predicts the pixels of the chrominance component from the pixels of the Y component.
  • the preset prediction mode is the intra prediction mode, the motion vector can be determined using the reference image of the image where the target image block is located, and prediction is performed based on the motion vector to obtain the prediction result.
  • a motion vector index indicating the motion vector used by the encoding side is encoded in the bitstream.
  • the motion vector includes a first motion vector and/or a second motion vector, and the motion vector index is, for example, gpm_idx0[x0][y0] and gpm_idx1[x0][y0]. It should be noted that gpm_idx0[x0][y0] and gpm_idx1[x0][y0] can also be sent in the merged data merge_data().
  • the target partition mode parameter when the prediction mode parameter is a target partition mode parameter, the target partition mode parameter also includes a motion vector index.
  • the motion vector index includes a first motion vector index and/or a second motion vector index.
  • the motion vector corresponds to a first motion vector and/or a second motion vector, and the motion vector may be required by using the inter prediction mode for the corresponding partition of the target image block.
  • the prediction mode indication information indicates that the first partition adopts inter prediction mode and the second partition adopts intra prediction mode
  • the first motion vector and/or the second motion vector is the motion vector of the first partition
  • prediction The mode indication information indicates that the second partition adopts the inter prediction mode and the first partition adopts the intra prediction mode
  • the first motion vector and/or the second motion vector is the motion vector of the second partition.
  • the prediction result set corresponding to the partition in the target image block may be determined by using the prediction mode corresponding to the partition of the target image block to determine the prediction result set corresponding to the partition of the target image block.
  • the set of prediction results includes a first set of prediction results of the first partition and/or a second set of prediction results of the second partition.
  • the first prediction result set may be determined according to the prediction mode used by the first partition in the target image block
  • the second prediction result set may be determined according to the prediction mode used by the second partition in the target image block.
  • the prediction result set of the target image block includes a first prediction result set and a second prediction result set.
  • the first prediction result set of the first partition (or the second prediction result set of the second partition) refers to the prediction based on the data in the first partition (or the second partition) according to the preset prediction mode.
  • the first prediction result set and the second prediction result set may be shared by the first partition and the second partition.
  • the prediction result set of the corresponding partition of the target image block is used to determine the prediction result of the target image block.
  • the prediction result of the target image block may be determined according to the first prediction result set of the first partition and/or the second prediction result set of the second partition.
  • the image processing solution provided by the embodiment of the present application predicts the target image block through the preset prediction mode, determines the prediction result set of partition pairs corresponding to the target image block, and then obtains the prediction result.
  • the selection range of the preset prediction mode is large. , it has high flexibility when used with the corresponding partitions of the target image block; and, according to the prediction result set of the corresponding partition in the target image block determined by the preset prediction mode, the prediction result set of the different partition games can be combined to ensure the prediction result of the target image block accuracy. As a result, a better balance can be achieved between the flexibility of using the preset prediction mode and the accuracy of the prediction results.
  • Figure 5 is a schematic flowchart of an image processing method according to a second embodiment.
  • the execution subject in this embodiment can be a computer device or a cluster composed of multiple computer devices.
  • the computer device can It may be an intelligent terminal (such as the aforementioned mobile terminal 100), or it may be a server.
  • the execution subject in this embodiment is an intelligent terminal as an example for explanation.
  • the prediction mode is the prediction mode used by the partitions divided by the dividing lines in the target image block
  • the prediction result set of the corresponding partition in the target image block can be determined according to the contents introduced in S501 and S502.
  • the prediction mode includes a first prediction mode and/or a second prediction mode. Since the prediction mode used by the corresponding partition may be the first prediction mode or the second prediction mode, the preset prediction mode may be the first prediction mode or the second prediction mode.
  • the first prediction mode is an inter prediction mode.
  • the prediction mode used by the partitions along the dividing lines of the target image block is the inter-frame prediction mode
  • the partitions divided by the dividing lines in the target image block include the first partition and/or the second partition.
  • the partition corresponding to the target image block is a partition using the inter prediction mode, and the partition may include a first partition and/or a second partition.
  • the first partition in the target image block uses the inter prediction mode, or the target image
  • the second partition in the block uses the inter prediction mode, or both the first and second partitions in the target image block use the inter prediction mode.
  • a prediction result set of the corresponding partition in the target image block may be determined based on the motion vector.
  • the first prediction result set is determined according to the motion vector of the first partition
  • the second prediction result set is determined according to the motion vector of the second partition.
  • the merging candidate list is obtained based on the spatial merging candidate list.
  • the merge candidate list is an inter-frame prediction candidate list and can be used to determine the first motion vector and/or the second motion vector when a unidirectional prediction candidate or a bidirectional prediction candidate is used.
  • the following introduces the construction process of the merge candidate list of the target image patch.
  • Figure 6a is a schematic diagram of adjacent block locations of an exemplary spatial domain merging candidate list provided by an embodiment of the present application.
  • the spatial merging candidate list can select up to 4 candidate motion vectors.
  • the construction order is: upper adjacent block B1, left adjacent block A1, upper right adjacent block B0, lower left adjacent block A0, upper left adjacent block Block B2, the reference frame corresponding position block col is the motion vector information of adjacent blocks arranged in sequence. It should be noted that B2 will only be considered if other locations are not available. After adding the lower left adjacent block A0, redundancy detection is required to ensure that there are no candidates with the same motion information in the list.
  • Figure 6b is a schematic diagram of an exemplary merging of candidate lists provided by an embodiment of the present application.
  • the merge candidate list includes the motion information of the five adjacent blocks shown in Figure 6a, with serial numbers 0, 1, 2, 3, and 4 respectively.
  • Each adjacent block contains bidirectional prediction motion vector information, that is, list 0 ( The motion vector information corresponding to list0) and list1 (list1) respectively.
  • inter prediction modes include unidirectional prediction mode and bidirectional prediction mode.
  • the unidirectional prediction mode or the bidirectional prediction mode can be used for partitions in the target image block that use the inter prediction mode.
  • the unidirectional prediction mode and/or the bidirectional prediction mode can be used by the unidirectional prediction used by a partition in the target image block.
  • bidirectional prediction indication information (such as a mark or index) to determine. Therefore, it can be determined according to the indication information whether a partition in the target image block specifically adopts the unidirectional prediction mode or the bidirectional prediction mode, and then further determines the unidirectional motion vector (the first motion vector or the third motion vector) of the partition from the merge candidate list.
  • first motion vector and second motion vector When the first partition (or second partition) in the target image block uses unidirectional prediction indication information, determine the first partition (or second partition) in the target image block. One partition (or the second partition) uses the unidirectional prediction mode, and determines the first motion vector or the second motion vector of the first partition (or the second partition) from the merge candidate list; when the first partition in the target image block (or second partition) when using bidirectional prediction indication information, determine that the first partition (or second partition) in the target image block uses the bidirectional prediction mode, and determine the first partition (or second partition) from the merge candidate list first motion vector and second motion vector.
  • the first motion vector and/or the second motion vector are both inter-frame predicted motion vectors. After the first motion vector and/or the second motion vector of the corresponding partition in the target image block are obtained by merging the candidate lists, the first motion vector can be calculated according to the first motion vector. The vector and/or the second motion vector perform motion compensation to determine the inter prediction value of the target image block with respect to the inter prediction motion vector, thereby obtaining a prediction result set, which contains the inter prediction value.
  • the prediction result set of the target image block can be determined in the above manner for each partition, and at this time
  • the prediction result set includes a first prediction result set and a second prediction result set, that is, the first prediction result set of the target image block is determined according to the motion vector of the first partition in the target image block, and the prediction result set is determined according to the motion vector of the second partition in the target image block.
  • the motion vector determines a second set of prediction results for the target image block.
  • both the first prediction result set and the second prediction result set are obtained through inter-frame prediction.
  • the target reference sampling point of the partition can also be used to determine the inter prediction corresponding to the partition.
  • Motion vector in mode if the B partition uses inter prediction mode, and there is one reference sample point x among the reference sampling points available for the B partition (for example, the first reference sampling point in Figure 6c) using inter prediction, then The motion vector of the reference sampling point x can be used to determine the motion vector in the inter prediction mode corresponding to the B partition. Alternatively, the motion vector of the reference sampling point x can be directly used as the motion vector of the B partition.
  • the reference sampling point x is a reference sampling point located at the center or middle among the available reference sampling points.
  • the prediction result set of the corresponding partition in the target image block is determined according to the value of the target reference sampling point of the corresponding partition in the target image block.
  • the prediction result set of the corresponding partition in the target image block is determined based on the pixel value of the brightness sampling point.
  • the pixel value of the brightness sampling point may be used as the pixel value of the sampling point in the partition.
  • the second prediction mode is an intra prediction mode.
  • the prediction mode used by the corresponding partition of the target image block is the first prediction mode or the second prediction mode, which is determined based on the prediction indication information.
  • the second prediction mode includes at least one type of second prediction mode.
  • the second prediction mode is an intra prediction mode, which includes multiple types of intra prediction modes.
  • the prediction mode types included in the intra prediction mode add more prediction directions to improve the accuracy of intra prediction.
  • the angle prediction modes included in VVC have been expanded to 65, while retaining the Planar mode and DC mode, for a total of 67 types of intra prediction modes. Details are shown in Table 4 below.
  • At least one type of intra prediction mode includes Planar mode, DC mode, and angle prediction mode.
  • Planar mode is used to solve gradient smooth texture areas.
  • DC mode is suitable for large flat areas.
  • Angle prediction modes include angle prediction mode 2 to angle prediction mode 66. Different angle prediction modes have different prediction directions to better Adapt textures to different orientations in video content.
  • the prediction mode when the prediction mode is the second prediction mode, it is also possible to: determine the second prediction of the target type used by the corresponding partition of the target image block from the at least one type of second prediction mode. model.
  • the partition of the target image block includes a first partition and/or a second partition.
  • the second prediction mode may be indicated by prediction indication information.
  • the prediction indication information of the first partition indicates that the prediction mode used by the first partition is an intra prediction mode
  • the intra prediction mode of the target type may be specifically determined.
  • the second prediction mode of the target type may be any one of Planar mode (ie, plane mode), DC mode, and 65 angle prediction modes.
  • the intra prediction mode of the target type used by the partition corresponding to the target image block is determined based on the most likely mode index within the frame.
  • the intra-frame most probable mode index refers to the index of the intra-frame prediction mode of the most likely target type to be used.
  • the intra-frame most probable mode index can be determined by constructing a most probable mode (Most Probable Mode, MPM) list. There are 6 prediction modes in the MPM list in the VVC. If the prediction mode of the current block only needs to encode its index in the MPM, the index is the most likely mode index in the frame. The amount of data can be effectively reduced and coding efficiency improved by constructing the most likely pattern list.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point, and the first reference sampling point and/or the second reference sampling point are different.
  • the selection of the first reference sampling point and/or the second reference sampling point is different.
  • the first reference sampling point and the second reference sampling point are different as follows:
  • the first reference sampling point is adjacent to the corresponding partition and not adjacent to another partition; the second reference sampling point is not adjacent to the corresponding partition and adjacent to the other partition.
  • the first reference sampling point is adjacent to the first partition and not adjacent to the second partition
  • the second reference sampling point is adjacent to the second partition and not adjacent to the first partition. Adjacent; when the corresponding partition in the target image block is the second partition, the first reference sampling point is adjacent to the second partition and not adjacent to the first partition, and the second reference sampling point is adjacent to the first partition and not adjacent to the first partition.
  • the two partitions are not adjacent.
  • a schematic diagram of an exemplary target reference sampling point shown in Figure 6c As shown in (1) in Figure 6c, the first reference sampling point (or the second reference sampling point) is different for different partitions.
  • the first reference sampling point may be a reference sampling point adjacent to the corresponding partition, and the second reference sampling point may be not adjacent to the corresponding partition in the target image block and adjacent to another partition other than the corresponding partition.
  • Reference sampling point For example, as shown in (1) in Figure 6c, for the first partition, the first reference sampling point may be a reference sampling point adjacent to the first partition, and the second reference sampling point may be a reference sampling point adjacent to the target image block. A reference sampling point that is not adjacent to the first partition and adjacent to the second partition.
  • the first reference sampling point may be a reference sampling point adjacent to the second partition, and the second reference sampling point may be a reference sampling point in the target image block that is not adjacent to the second partition and is adjacent to the first partition. adjacent reference sampling points.
  • the first reference sampling point or the second reference sampling point also includes reference sampling points that are not adjacent to the corresponding partition.
  • the first reference sampling point when the corresponding partition in the target image block is the first partition, the first reference sampling point includes a reference sampling point adjacent to the first partition and not adjacent to the second partition.
  • the two reference sampling points include reference sampling points that are adjacent to the second partition and not adjacent to the first partition, and reference sampling points that are not adjacent to neither the first partition nor the second partition; when the corresponding partition in the target image block is During the second partition, the first reference sampling points include reference sampling points that are adjacent to the second partition and not adjacent to the first partition, and reference sampling points that are not adjacent to neither the first partition nor the second partition.
  • the reference sampling points include reference sampling points adjacent to the first partition and not adjacent to the second partition.
  • the positional relationship of the first reference sampling point relative to the corresponding partition is different from the positional relationship of the second reference sampling point relative to the corresponding partition.
  • the first reference sampling point or the second reference sampling point is a pixel point adjacent to the coding tree unit where the target image block is located, and not adjacent to the target image block.
  • the target image block is a coding unit
  • the coding unit may be any coding unit being coded in the coding tree unit.
  • VVC coding one frame of image can be divided into multiple coding tree units for sequential encoding, and each coding tree unit is divided into multiple coding units for sequential encoding.
  • the coding unit in the next coding tree unit is then coded, as shown in Figure 6d.
  • the target reference sampling point includes the first reference sampling point and the second reference sampling point adjacent to the coding tree unit; when the target image block is the coding tree unit
  • the target reference sampling point includes the reference sampling point adjacent to the coding tree unit and/or the reference sampling point adjacent to the coding unit, as shown in (1) in Figure 6c,
  • the first reference sampling point and the second reference sampling point included in the target reference sampling point are both adjacent to the coding unit. These reference sampling points are already encoded pixels.
  • the first partition in the target image block can use the first prediction mode and the second partition can use the second prediction mode
  • the contents described in S501 and S502 can be executed at the same time, and the target image block in S501
  • the corresponding partition is the first partition
  • the corresponding partition of the target image block in S502 is the second partition.
  • the first prediction mode can be used for the second partition
  • the second prediction mode can be used for the first partition
  • the contents described in S501 and S502 can be executed simultaneously
  • the corresponding partition of the target image block in S501 is the second partition.
  • the corresponding partition of the target image block in S502 is the first partition.
  • the prediction result set of the corresponding partition of the target image block is used to determine the prediction result of the target image block.
  • partition weight is determined based on the angle index and the distance index included in the target partition mode parameters.
  • Partition weight refers to the weight corresponding to the two partitions included in the image block. The partition weight is used to weight each pixel point in the corresponding partition to obtain a weighted predicted pixel value, recorded as predSamples. The weighted predicted pixel value of the corresponding partition can be used as forecast result.
  • the straight line equation of the dividing line can be obtained according to the angle index angleIdx and the distance index distanceIdx.
  • the straight line equation in the straight line equation can be determined according to the angle index angleIdx.
  • ⁇ in the straight line equation can be determined according to the distance index distanceIdx.
  • (x c , y c ) is the coordinate of any sampling point in the target image block.
  • the angle index and distance index included in the target division mode parameters can determine the distance between each pixel point in the target image block and the dividing line, and the weight corresponding to each pixel value of the target image block can be determined based on this distance. .
  • set different weights according to the distance of the pixel point (x c , y c ) relative to the dividing line For example, if the distance between the pixel point (x c , y c ) and the dividing line is greater than or equal to the set distance threshold , then the weight corresponding to the pixel point (x c , y c ) is set to K1, otherwise, the weight corresponding to the pixel point (x c , y c ) is set to K2. It is obvious that the pixel points smaller than the set distance threshold are located near the dividing line, and the pixel points larger than the set distance threshold are far away from the dividing line.
  • the above rules are used to set the weights to obtain the partition weights.
  • different fixed weights can be set. It can be seen that the weight of each pixel value is not only related to the distance from the dividing line, but also to the location of each pixel. Partition related.
  • the partition weight By setting the partition weight in this way, different degrees of attention can be given to the pixels in the two image areas bounded by the dividing line in the target image block, and the pixels corresponding to the pixels close to the dividing line and those not close to the dividing line can
  • the weights can also be different.
  • the pixels closer to the dividing line have greater weights. In this way, the two partitions of the target image block can be better fused along the edge of the dividing line to obtain the prediction result.
  • the first prediction result set is obtained by using a first prediction mode (such as inter prediction mode), or is obtained by using a first prediction mode (such as intra prediction mode), and the second prediction result set is the same as reason.
  • a first prediction mode such as inter prediction mode
  • a first prediction mode such as intra prediction mode
  • For the first partition it may be: based on the first weight, the first prediction result of the sampling point included in the target image block in the first prediction result set, and the second prediction result of the sampling point in the second prediction result set. At least one of the prediction results determines the prediction result of the sampling point in the first partition included in the target image block.
  • the first weight may be a weight set corresponding to the first partition, which may be called the first weight set, including ⁇ w 11, w 12 ⁇ .
  • the rule for determining the weight is determined based on the distance between the sampling point and the dividing line, which respectively represents the distance between the sampling point and the dividing line.
  • the weight corresponding to the dividing line and the weight corresponding to the distance from the dividing line are divided by the distance threshold.
  • Using the first weight perform a weighted summation process on the first prediction result corresponding to the sampling point in the first partition included in the target image block in the first prediction result set and the corresponding second prediction result in the second prediction result set. , to obtain the fused predicted value, that is, the predicted result of the sampling point in the first partition.
  • (x c1 , y c1 ) represents the sampling point in the first partition
  • P 11 represents the first prediction result of the sampling point in the first partition in the first prediction result set
  • P 12 represents the sampling of the first partition
  • w 11 can be set to K1, and w 12 corresponds to (1-K1); or w 11 is set to K2, w 12 corresponds to (1-K2).
  • For the second partition it may be: based on the second weight, the first prediction result of the sampling point included in the target image block in the first prediction result set, and the second prediction result of the sampling point in the second prediction result set. At least one of the prediction results determines the prediction result of the sampling point in the second partition included in the target image block.
  • the second weight is the set of weights corresponding to the second partition, which can be called the second weight set, including ⁇ w 21, w 22 ⁇ , which respectively represent the weight corresponding to the dividing line in the second partition and the weight corresponding to far away from the dividing line.
  • the distance threshold can be the same as the distance threshold used in the first partition, or it can be different.
  • the sampling points in the second partition included in the target image block have corresponding prediction results in both the first prediction result set and the second prediction result set.
  • the second weight can be used to include the target image block
  • the first prediction result corresponding to the sampling point in the second partition in the first prediction result set and the second prediction result corresponding to the second prediction result set are weighted and summed to obtain the fused prediction value, that is, the Prediction results of sampling points in the binary partition.
  • the corresponding prediction results can be determined in the above manner, and then the prediction results of the first partition and the prediction results of the second partition are obtained.
  • the prediction results include the prediction results of all sampling points in the first partition
  • the prediction results of the second partition include the prediction results of all sampling points in the second partition.
  • the prediction results of the first partition and the prediction results of the second partition can be combined to obtain the prediction results of the sampling points of all partitions, and the prediction results are the prediction results of the target image block. Or edge fuse the prediction results of the two partitions to obtain the prediction result of the target image block.
  • the image processing solution provided by the embodiment of the present application can determine the prediction result set according to different information of the corresponding partitions of the target image block in different prediction modes. That is, when the prediction mode is the first prediction mode, the prediction result set can be determined according to the first prediction mode.
  • the first prediction result set is determined by the motion vectors of the partitions in the target image block using the first prediction mode.
  • the prediction mode is the second prediction mode, it can be determined based on the target reference sampling points of the partitions in the target image block using the second prediction mode.
  • a second set of prediction results, and both the first prediction mode and the second prediction mode include multiple types, especially the second prediction mode has a wider range of choices and more combinations with the division modes used by the target image block, and The accuracy of prediction results has been improved.
  • Figure 7 is a schematic flowchart of an image processing method according to a third embodiment.
  • the execution subject in this embodiment can be a computer device or a cluster composed of multiple computer devices.
  • the computer device can It may be an intelligent terminal (such as the aforementioned mobile terminal 100), or it may be a server.
  • the execution subject in this embodiment is an intelligent terminal as an example for explanation.
  • the target image block is determined according to the target reference sampling point of the corresponding partition in the target image block.
  • the prediction result set corresponding to the partition in please refer to the content introduced in S701 and S702 below.
  • the target image block includes a first partition and/or a second partition, and the corresponding partition in the target image block is the first partition or the second partition.
  • the first reference sampling point and/or the second reference sampling point of the first partition, and the first reference sampling point and/or the second reference point of the second partition may be determined. Sampling points, since the logic of the reference sampling points determined for different partitions is similar, only the contents of the first reference sampling point and/or the second reference sampling point are different for different partitions. Therefore, in this embodiment, determining the first reference sampling point and/or the second reference sampling point of a partition (for example, the first partition) is taken as an example for explanation.
  • the implementation of S701 may be: based on the positional relationship between the corresponding partition in the target image block and the boundary sampling point of the dividing line used by the target image block in the target image block, determine The first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block.
  • the partition corresponding to the target image block here refers to the partition in the target image block using the second prediction mode.
  • the second prediction mode is an intra prediction mode.
  • the positional relationship between the corresponding partition and the boundary sampling point in the target image block refers to the relative positional relationship between the sampling point in the partition using the second prediction mode and the boundary sampling point. According to the positional relationship, the first partition or the first partition can be determined.
  • the dividing lines used by the target image block pass through different boundaries of the target image block to divide the target image block into different image areas, which are called first partitions and second partitions here.
  • the target image block includes at least one sampling point, and optionally, the sampling point is a pixel point.
  • the boundary sampling points of the dividing line in the target image block refer to the samples passed by the dividing line among the N sampling points adjacent to the encoded pixel points in the target image block.
  • point, N is a positive integer.
  • Figure 8a it is a schematic diagram of the boundary sampling point where the dividing line is located.
  • the dividing line divides the target image block into partition A and partition B, and the dividing line passes through the upper boundary and the left boundary of the target image block, so the boundary sampling points include boundary sampling point 1 and boundary sampling point 2.
  • the following steps may be included: determining the boundary through which the dividing line used by the target image block passes according to the boundary mapping table; determining the boundary sampling point according to the boundary through which the dividing line passes.
  • the boundary mapping table includes a mapping relationship between each dividing line passing through a specified boundary in the target image block, where the specified boundary includes a first boundary and/or a second boundary.
  • the first boundary is the upper boundary and the second boundary is the left boundary.
  • the mapping relationship between each dividing line passing through the specified boundary in the target image block may be used to indicate the dividing index of the dividing line and whether the dividing line passes through the upper boundary and the left boundary of the target image block.
  • the boundary mapping table is shown in Table 5 below.
  • This boundary mapping table is a dividing line index corresponding to 64 dividing modes in the geometric dividing mode, and records information about each dividing line index and whether the dividing line passes through the left boundary or the upper boundary. Therefore, the boundary through which the dividing line used by the target image block passes can be quickly determined through the boundary mapping table, thereby determining the boundary sampling point.
  • the boundary sampling point includes the first boundary sampling point; and/or, when the target image block uses When the dividing line passes through the second boundary of the target image block, it is determined that the boundary sampling point includes the second boundary sampling point.
  • the boundary mapping table can be used to determine the situation where the dividing line passes through the specified boundary, and then determine the content included in the boundary sampling points.
  • the first boundary is the upper boundary and the second boundary is the left boundary.
  • the boundary sampling points include the upper boundary sampling points, and/or when the dividing line passes through the left boundary of the target image block, the boundary sampling points include the left boundary sampling points.
  • the dividing line may pass through the upper boundary and the left boundary, or may pass through the upper boundary or the left boundary.
  • the boundary sampling points include upper boundary sampling points and/or left boundary sampling points.
  • the boundary sampling point includes at least one boundary sampling point.
  • the dividing line may also pass through any of the left border, right border, and lower border at the same time.
  • the dividing line may also pass through the upper border, the right border, and the lower border at the same time. any of them.
  • the segmentation situation of different segmentation lines in the image block is as shown in the aforementioned Figure 4c.
  • the method for determining the boundary sampling point includes: determining the dividing line equation according to the target division mode parameter of the target image block; determining at least one boundary sampling point according to the boundary reference point and the dividing line equation. .
  • the target division mode parameter of the target image block includes at least one of the following: angle index, distance index and division index.
  • the target partition mode parameter may be a geometric partition mode parameter.
  • the dividing line equation is determined according to the angle index and the distance index included in the target dividing mode parameters.
  • the dividing line equation refers to the straight line equation of the dividing line. According to the angle index angleIdx, the dividing line equation is determined in the angle mapping table. and And determine ⁇ in the dividing line equation according to the distance index distanceIdx.
  • the expression for the dividing line equation is as follows:
  • (x c , y c ) is the coordinate of any sampling point in the target image block.
  • At least one boundary sampling point can be determined based on the boundary reference point and the dividing line equation.
  • the boundary reference point is a sampling point arranged first in coding order in the target image block. From a positional point of view, the boundary reference point is the sampling point at the upper left position of the target image block.
  • the position of the boundary reference point is recorded as (xCb, yCb).
  • the position coordinates of at least one boundary sampling point are determined according to the boundary reference point.
  • At least one boundary sampling point includes a first boundary sampling point and/or a second boundary sampling point. If the dividing line passes through the second boundary (the second boundary is the left boundary), then the boundary sampling points include the second boundary sampling point, and the position coordinates of the second boundary sampling point are (x_gpm_left,y_gpm_left); if the dividing line also passes through the first boundary (The first boundary is the upper boundary) Then the boundary sampling point also includes the first boundary sampling point, and the position coordinates of the first boundary sampling point are (x_gpm_above, y_gpm_above).
  • x_gpm_left xCb
  • the first boundary sampling point and the boundary reference point are on the same x-axis
  • the y_gpm_left of the second boundary sampling point and the x_gpm_above of the first boundary sampling point can be determined through the dividing line equation.
  • the dividing line does not pass through the second boundary of the target image block (for example, the left boundary), then the second boundary sample The point does not exist.
  • the value of x_gpm_above exceeds the predetermined range (referring to the x value of the last critical sampling point arranged on the first boundary)
  • the dividing line does not pass through the target image block.
  • the first boundary such as the upper boundary
  • the first boundary sampling point does not exist.
  • the y1 of the critical sampling point 1 can be used as a measure of whether the second boundary sampling point exists. That is to say, the y_gpm_left calculated based on the dividing line is compared with y1. If y_gpm_left is greater than y1, the second boundary sampling point does not exist. Otherwise, then exist.
  • similarly x2 can be used as a judgment value to determine whether x_gpm_above exceeds the predetermined range, thereby determining whether the first boundary sampling point exists.
  • the boundary through which the dividing line passes is determined according to the boundary mapping table, you can first determine whether a boundary sampling point that meets the conditions exists, and then obtain the position coordinates of the corresponding boundary sampling point in the above manner. For example, if the boundary mapping table is queried according to the dividing line index and it is determined that the dividing line used by the target image block passes through the upper boundary and the left boundary, then the upper boundary sampling point and the left boundary sampling point can be determined based on the dividing line equation and the boundary reference point. specific location.
  • the first reference sampling point and/or the second reference sampling point may be determined based on the positional relationship between the boundary sampling point and the corresponding partition of the target image block. In one embodiment, the following steps may be included: determining the first coordinate range and/or the second coordinate range according to the at least one boundary sampling point; determining the first coordinate range and/or the second coordinate range. The first reference sampling point and/or the second reference sampling point.
  • At least one boundary sampling point includes a first boundary sampling point and/or a second boundary sampling point.
  • at least one of the following methods is included: determining the first coordinate range according to the first boundary sampling point and/or the second boundary sampling point, and determining the second coordinate range according to the first boundary sampling point and/or the second boundary sampling point. scope.
  • At least one boundary sampling point includes a first boundary sampling point and a second boundary sampling point, and the position coordinates of the first boundary sampling point are (x_gpm_above, y_gpm_above), and the position coordinates of the second boundary sampling point are (x_gpm_left, y_gpm_left).
  • the first coordinate range and the second coordinate range are illustrated.
  • the reference sampling points within the first coordinate range include the reference sampling point located above and to the left of the second boundary sampling point, and the reference sampling point located above and to the left of the first boundary sampling point;
  • the reference sampling points include a reference sampling point located above and to the right of the second boundary sampling point, and a reference sampling point located below and to the left of the first boundary sampling point.
  • the reference sampling point located within the first coordinate range may be determined as the first reference sampling point, and/or The reference sampling point located within the second coordinate range is determined as the second reference sampling point.
  • the reference sampling point located in the second coordinate range can be determined as The first reference sampling point, and/or the reference sampling point located within the first coordinate range is determined as the second reference sampling point.
  • the first reference sampling point and/or the second reference sampling point may be available or unavailable.
  • the first reference sampling point refers to the reference sampling point that is available in the partition corresponding to the target image block
  • the second reference sampling point refers to the reference sampling point that is unavailable in the corresponding partition of the target image block.
  • the A partition adopts the intra prediction mode.
  • the sampling value of the upper reference sampling point located on the right side of gpm_above and the original sampling value in the A partition are greatly different.
  • the upper reference sampling points located on the right side of gpm_above are all reference sampling points that are unavailable for the A partition.
  • the left reference sampling points located below gpm_lef are all reference sampling points that are unavailable for partition A. It should be noted that for partition A, the available reference sampling points are the reference sampling points immediately adjacent to partition A.
  • the upper reference sampling points located to the left of gpm_above are all reference samples that are unavailable for the B partition.
  • the left reference sampling points located above gpm_lef are all reference sampling points that are unavailable for the B partition. It should be noted that for partition B, the available reference sampling points are the reference sampling points immediately adjacent to partition B.
  • the implementation of S701 may also be: based on the distance between the sampling point of the corresponding partition in the target image block and the dividing line. , determine the partition range of the corresponding partition in the target image block; determine the first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block based on the coordinate range of the first sampling point and the partition range. ; Or, based on the distance information between the first sampling point adjacent to the target image block and the dividing line, determine the first sampling point as the first reference sampling point of the corresponding partition in the target image block. Or the second reference sampling point.
  • the equation of the dividing line can first be determined.
  • the distance between the sampling point of the corresponding partition in the target image block and the dividing line can be determined. Assume that the sampling point of the corresponding partition in the target image block is (x c , y c ). According to the dividing line equation, the distance from the sampling point to the dividing line can be obtained, as shown in the following formula.
  • d(x c , y c ) is the distance between the sampling point in the target image block and the used dividing line.
  • Sampling points can refer to pixel points.
  • the partition corresponding to the target image block is a partition using the first prediction mode (eg, intra prediction mode).
  • the first prediction mode eg, intra prediction mode
  • a partition whose distance from a specific position of the target image block or the position of an adjacent image block of the target image block is less than a preset distance may be determined as the first partition, and A partition whose distance between specific positions is greater than the preset distance is determined as a second partition.
  • a partition whose distance from the position of adjacent image blocks of the target image block is less than a preset distance may be determined as the first partition, and the distance between the position of the adjacent image block and the position of the adjacent image block of the target image block may be determined as the first partition.
  • the partition whose distance is greater than the preset distance is determined as the second partition.
  • the specific position of the target image block corresponds to the coordinates of the upper left corner sampling point of the target image block
  • the positions of the adjacent image blocks of the target image block correspond to the spatial adjacent blocks of the target image block in the constructed spatial merging candidate list. coordinate.
  • the spatial adjacent blocks are the upper adjacent block B1, the left adjacent block A1, the upper right adjacent block B0, the lower left adjacent block A0, and the upper left adjacent block B2.
  • the first sampling point is a reference sampling point adjacent to the target image block, the first sampling point includes at least one and the first sampling point is a coded sampling point.
  • the first sampling point can be used as the first reference sampling point or the second reference sampling point, and can be determined in either of the following two ways:
  • the first sampling point adjacent to the corresponding partition may be determined as the first reference sampling point of the corresponding partition, and the first sampling point not adjacent to the corresponding partition may be determined as the second reference sampling point of the corresponding partition.
  • the first reference sampling point (or the second reference sampling point) is an available or unavailable reference sampling point.
  • Figure 8d is a schematic diagram of the division of target reference sampling points according to an embodiment of the present application. As shown in Figure 8d, the image block includes partition A and partition B.
  • the reference sampling points include reference sampling points adjacent to partition A, reference sampling points adjacent to partition B, and reference sampling points that are not adjacent to either partition A/B. Sampling point.
  • the reference sampling point that is not adjacent to any A/B partition is a reference sampling point that is adjacent to the coding tree unit where the target image block is located. If the intra prediction mode partition in the target image block is partition A, then determine the first reference sampling point corresponding to partition A as the reference sampling point adjacent to partition A, and the second reference sampling point corresponding to partition A as partition B. Adjacent reference sampling points and reference sampling points that are not adjacent to the A/B partitions.
  • the partition in the target image block that uses the intra prediction mode is the B partition
  • the second reference sampling point corresponding to partition B is the reference sampling point adjacent to partition A. It can be seen that the first reference sampling point also includes reference sampling points that are not adjacent to the corresponding partition.
  • the distance information includes a sign of the distance between the first sampling point and the dividing line.
  • the first sampling point is a reference sampling point adjacent to the target image block. Whether the reference sampling point is adjacent to the first partition or the second partition can be determined by calculating the sign of the distance from the reference sampling point to the dividing line. For example, if the sign of the distance from the reference sampling point to the dividing line is the same as the sign of the distance from the sampling point in the first partition to the dividing line, then the reference sampling point is adjacent to the first partition.
  • the reference sampling point is different from the sign of the distance between the reference sampling point and the dividing line.
  • a partition is not adjacent.
  • the sign of the distance here can be the positive or negative sign used to characterize the distance.
  • the first reference sample and the second reference sample corresponding to the partition are determined according to the partition corresponding to the target image block in the adopted second prediction mode.
  • the second prediction mode is intra prediction mode.
  • the reference sampling points adjacent to the first partition are available reference sampling points of the first partition, and the reference sampling points not adjacent to the first partition are unavailable references of the first partition. Sampling point.
  • the reference sampling points that are not adjacent to the first partition are available reference sampling points for the second partition, and the reference sampling points adjacent to the first partition are unavailable references for the second partition. Sampling point.
  • S702 Determine a set of prediction results for corresponding partitions in the target image block according to the first reference sampling point and/or the second reference sampling point.
  • the two reference sampling points can be further used for intra-frame prediction to obtain a prediction result set of the target image block.
  • one of the reference sampling points is not available for a certain partition, it can be used in the actual prediction process of intra prediction through corresponding processing, and the encoding quality is guaranteed, so that the target image block is divided into different When predicting an image area, you can flexibly select from different types of second prediction modes.
  • the second prediction mode is an intra prediction mode
  • any one of multiple intra prediction mode candidates can be selected.
  • the selection range of intra prediction mode types used for dividing modes is fully expanded, ensuring the coding quality while improving the flexibility and range of selection.
  • step S702 includes the following content: (1) filling the second reference sampling point according to the first reference sampling point to obtain a filled reference sampling point; (2) filling the second reference sampling point according to the first reference sampling point; The reference sampling point and the filled reference sampling point determine a prediction result set corresponding to the partition of the target image block.
  • the first reference sampling point is an available reference sampling point for the corresponding partition of the target image block
  • the second reference sampling point is an unavailable reference sampling point for the corresponding partition of the target image block
  • Filling the unavailable reference sampling points can replace the unavailable reference sampling points with other pixels to obtain the filled reference sampling points, thereby making the second reference sampling points available for use in the actual processing process, that is, :
  • the determination process of the prediction result set corresponding to the partition of the target image block is jointly participated or acted upon by the first reference sampling point and the filled reference sampling point.
  • S7021 Determine at least one filling reference sampling point in the first reference sampling point.
  • the at least one filling reference sampling point may be an adjacent one or at least one first reference sampling point, such as the first reference sampling point closest to the second reference sampling point, or at least one sequentially arranged adjacent to the second reference sampling point.
  • the at least one filling reference sampling point may also be any one or more of the first reference sampling points, such as the first reference sampling point.
  • first reference sampling point and the second reference sampling point include different sampling points for different partitions of the target image block, therefore, at least one filling reference sampling point is different for the target image block.
  • the divisions are also different.
  • FIGS 9a and 9b are schematic diagrams of some filled reference sampling points provided by embodiments of the present application.
  • partition A is a partition in the target image block that uses intra prediction mode.
  • the reference sampling points adjacent to partition A are available reference sampling points for partition A, which are different from partition A.
  • the adjacent reference sampling point is an unavailable reference sampling point in partition A.
  • At least one fill reference sampling point includes two available nearest reference sampling points among the reference sampling points available in the A partition.
  • the B partition is a partition using the intra prediction mode in the target image block.
  • the reference sampling points that are not adjacent to the A partition are available reference sampling points for the B partition.
  • the adjacent reference sampling point is an unavailable reference sampling point in partition B.
  • At least one filling reference sampling point includes two available nearest reference sampling points among the available reference sampling points in the B partition.
  • S7022 Determine the filling value of the second reference sampling point based on the sampling value of the at least one filling reference sampling point.
  • the sampling value of the nearest first reference sampling point can be used as the filling value of the second reference sampling point.
  • the filling value of the second reference sampling point may be determined based on at least one adjacent first reference sampling point. For example, the average value of at least one adjacent first reference sampling point is used as the filling value of the second reference sampling point.
  • different weights can be given to at least one filling reference sampling point, so that the influence of filling reference sampling points at different positions on the filling value of the second reference sampling point can be taken into account.
  • each of the filling reference sampling points is each of the at least one filling reference sampling point. Therefore, each filling reference sampling point corresponds to a filling weight. For example, three filling reference sampling points correspond to three filling weights respectively. Based on the filling weight, the sample values of each filling reference sampling point can be weighted and summed. The obtained weighted summation value can be used as the filling of the second reference sampling point. top up.
  • the positional relationship includes the distance between each filling reference sampling point and the second reference sampling point.
  • the distance here may refer to the straight-line distance between two reference sampling points, and the filling weight may be determined based on the distance between the filling reference sampling point and the second reference sampling point. Since the distance between the second reference sampling point and the second reference sampling point is greater, The correlation between distant filling reference sampling points is smaller, so the farther away the filling reference sampling points are, the smaller the filling weight can be set, so that the filling weight decreases as the distance increases.
  • the filling reference sampling point with strong correlation can play a greater role in adjusting the difference between the second reference sampling point and the sampling points in the partition, and obtain better results. Filling quality, thereby better reducing residuals and improving encoding quality.
  • the first reference sampling point includes a first reference sampling point adjacent to the first boundary of the target image block, and/or a first reference sampling point adjacent to the second boundary of the target image block.
  • the first boundary is the upper boundary and the second boundary is the left boundary.
  • the first reference sampling points can be divided into two categories according to their proximity to the boundary of the target image block, namely, the first reference sampling points adjacent to the upper boundary and the first reference sampling points adjacent to the left boundary.
  • the available reference sampling points (ie, the first reference sampling points) in the B partition include reference sampling points that are not adjacent to either the A or B partitions.
  • the at least one filling reference sampling point includes the first filling reference sample among the first reference sampling points adjacent to the first boundary. point, and/or a second filled reference sample point among the first reference sample points adjacent to the second boundary. That is to say, at least one filling reference sampling point includes a first filling reference sampling point and a second filling reference sampling point, and the first filling reference sampling point is one or more of the first reference sampling points adjacent to the upper boundary, The second filling reference sample point is one or more of the first reference sample points adjacent to the left boundary.
  • the filling value of the second reference sampling point can optionally be determined based on the sampling value of the first filling reference sampling point and the first filling reference sampling point.
  • the corresponding boundary includes the first boundary and/or the second boundary of the target image block.
  • the first boundary is the upper boundary and the second boundary is the left boundary.
  • the filling value determination for the second reference sampling point includes any one or more of the following situations: determining the filling value based on the sampling value of the first filling reference sampling point and the filling weight of the first filling reference sampling point and The filling value of the second reference sampling point adjacent to the first boundary; the filling value of the second reference sampling point adjacent to the second boundary is determined based on the sampling value of the second filling reference sampling point and the filling weight of the second filling reference sampling point.
  • Recharge determine all second reference samples based on the sampling value of the first filling reference sampling point and the filling weight of the first filling reference sampling point, and the sampling value of the second filling reference sampling point and the filling weight of the second filling reference sampling point.
  • Filling values of points, all second reference sample points include second reference sample points adjacent to the first boundary, and/or second reference samples adjacent to the second boundary.
  • At least one filling reference sampling point includes a first filling reference sampling point and a second filling reference sampling point.
  • the first filling reference sampling point includes 3 adjacent available reference sampling points among the available reference sampling points adjacent to the upper boundary of the B partition (the available reference sampling point is the first reference sampling point), and the left boundary of the B partition Among the three adjacent available reference sampling points, these filling sampling points are adjacent to the unavailable reference sampling point (ie, the second reference sampling point).
  • the first filling reference sampling point may be used to fill the first reference sampling point adjacent to the upper boundary of the A partition, and the filling values of each first reference sampling point adjacent to the upper boundary of the A partition are the same.
  • Second fill reference The sampling points can be used to fill the first reference sampling points adjacent to the left boundary of the A partition, and the filling values of each first reference sampling point adjacent to the left boundary of the A partition are the same.
  • At least one filling reference sampling point also includes a first filling reference sampling point and a second filling reference sampling point;
  • the first filling reference sampling point includes available references that are not adjacent to each other and adjacent to the upper boundary.
  • the sampling points, the second filling reference sampling points include available reference sampling points that are not adjacent to each other and adjacent to the left boundary.
  • the second reference sampling point on the same boundary can be filled according to the first filling reference sampling point: the filling value is obtained by performing a weighted sum of the filling weight and the sampling value of the first filling reference sampling point to fill the second reference sampling point. .
  • the finally obtained sample value of the filled second reference sampling point on the boundary is the same.
  • the sampling value of the first filling reference sampling point may also be directly used as the filling value, and the filling may be alternately performed in a preset direction.
  • the first filling reference sampling point includes sampling points 1, 2, and 3, then the sampling value of sampling point 1 can be filled to the first second reference sampling point from right to left, and the sampling value of sampling point 2 can be filled from right to left.
  • the second second reference sampling point to the left, the sampling value of sampling point 2 fills the third second reference sampling point from right to left, and sampling point 1 then fills the fourth second reference sample from right to left. points, looping in turn until the second reference sampling point adjacent to the upper boundary is filled.
  • the filling value of the second reference sampling point is also the average value between the first filling reference sampling point and the second filling reference sampling point.
  • the first filling reference sampling point and the second filling reference sampling point have different distances from the second reference sampling point to be filled, and the set filling weights are also different.
  • S7023 Fill the second reference sampling point based on the filling value to obtain a filled reference sampling point.
  • the filling of the second reference sampling point based on the filling value may be a process of assignment, for example, assigning the sampling value of the nearest first reference sampling point to the second reference point, or assigning the sampling value of at least one adjacent first reference sampling point to the second reference point.
  • the average sampling value is assigned to the second reference sampling point to obtain the filled reference sampling point.
  • the sample value in the filled reference sampling point is the filling value.
  • the prediction result set of the corresponding partition of the target image block may be determined based on the first reference sampling point and the filled reference sampling points. In this way, when determining the prediction result set corresponding to the partition of the target image block, even if there are unavailable reference sampling points (ie, the second reference sampling point), by processing the unavailable reference sampling points, compared with using without doing anything In the case of prediction based on the second reference point, the unsatisfactory prediction results will be effectively improved.
  • the solution provided by the embodiment of the present application can also be applied to non-square coding blocks. Since the non-square coding blocks adopt the wide-angle intra prediction mode, when the coding block adopts the GPM mode, the wide-angle intra prediction mode It can also be combined with GPM mode, thereby expanding the types of intra prediction modes combined with GPM mode.
  • the intra prediction mode corresponding to the intra prediction direction indicated by the dotted arrow is the wide-angle intra prediction mode.
  • the image processing solution provided by the embodiment of the present application predicts the target image block using the second prediction mode (such as intra prediction mode), it can determine the first reference sampling point corresponding to the partition using the second prediction mode. and/or second reference sampling points.
  • These reference sampling points include available reference sampling points and unavailable reference sampling points. Filling the unavailable reference sampling points with the available reference sampling points can reduce the original unavailable reference sampling points.
  • the difference in the original value between the reference sampling point and the sampling point in the partition using the second prediction mode, in the actual use process turns the originally unavailable reference sampling point into an available reference sampling point, and predicts the image block used in the process to obtain smaller residuals and improve coding quality. In this way, the poor processing effect of the original second prediction mode has been effectively improved.
  • Any type of second prediction mode does not have to be limited to scenarios with poor prediction effects, so that multiple types of second prediction modes can be used.
  • the selection range of prediction modes is enlarged, the flexibility is higher, and the encoding quality can be ensured.
  • the intra prediction mode available for the target image block in GPM mode can be effectively expanded by ensuring the coding quality, the combination of GPM mode and intra prediction mode is more flexible, thus achieving a balance between flexibility and coding quality. Better balance.
  • Figure 10 is a schematic flowchart of an image processing method according to a fourth embodiment.
  • the execution subject in this embodiment can be a computer device or a cluster composed of multiple computer devices.
  • the computer device can It may be an intelligent terminal (such as the aforementioned mobile terminal 100), or it may be a server.
  • the execution subject in this embodiment is an intelligent terminal as an example for explanation.
  • an optional implementation of step S1001 may be: determining the target reference sampling point through the positional relationship between the reference sampling point and the sampling point in the image block partition.
  • the image block refers to the image block currently being encoded in the input video image (ie, the video frame), which may be referred to as the current block or the current image block or the current coding block.
  • the image block here corresponds to the target image block mentioned in the foregoing embodiment.
  • the image block may be a target image block.
  • the image block partition is an image area divided by a dividing line used by the image block.
  • the image block partition includes a first partition and/or a second partition.
  • the sampling points in the image block partition include sampling points of the first partition and/or Sampling points for the second partition.
  • the reference sampling points include coded sampling points adjacent to the image block partition and/or coded sampling points adjacent to the reference image block where the image block is located.
  • the reference image block is a coding tree unit.
  • the reference image block includes at least one image block. When at least one image block is sequentially encoded, the encoding of the reference image block is completed, as shown in FIG. 6d above.
  • the positional relationship between the reference sampling point and the sampling point in the image block partition can be a relative orientation relationship, or can also be characterized by distance information. Whether the reference sampling point is available or unavailable for the image block partition can be determined based on the positional relationship between the reference sampling point and the sampling point of the image block partition, and then the target reference sampling point can be determined.
  • the target reference sampling points may include reference sampling points that are available in the image block partition and/or reference sampling points that are unavailable in the image block partition.
  • determining the target reference sampling point through the positional relationship between the reference sampling point and the sampling point in the image block partition may include the following steps 1) and 2):
  • the at least one boundary sampling point includes a first boundary sampling point and/or a second boundary sampling point.
  • the at least one boundary sampling point includes the first boundary.
  • the first boundary is the upper boundary
  • the second boundary is the left boundary
  • the first boundary sampling point is the upper boundary sampling point
  • the second boundary sampling point is the left boundary sampling point.
  • the method of determining at least one boundary sampling point includes: determining a dividing line equation according to a target division mode parameter of an image block; determining at least one boundary sampling point based on a boundary reference point and the dividing line equation.
  • At least one boundary sampling point includes a first boundary sampling point and/or a second boundary sampling point.
  • Optional implementations of step 2) include: determining a first coordinate range based on the first boundary sampling point, and/or determining a second coordinate range based on the second boundary sampling point; The target reference sampling point is determined based on the positional relationship between a coordinate range, and/or based on the positional relationship between the reference sampling point and the second coordinate range.
  • the first coordinate range and/or the second coordinate range are used to delimit the sampling area of the target reference sampling point.
  • the area where the reference sampling point of the upper side point is located is determined as the first coordinate range.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point, and the sampling values of the target reference sampling point are different for different partitions of the image block.
  • the reference sampling point within the first coordinate range can be used as the first reference sampling point
  • the reference sampling point within the second coordinate range can be used as the first reference sampling point. point as the second reference sampling point.
  • determining the target reference sampling point through the positional relationship between the reference sampling point and the sampling point in the image block partition may include the following steps: determining the partition range according to the sampling point in the image block partition; according to the reference sampling point and the The location relationship within the partition range determines the target reference sampling point.
  • the partition range can be delimited based on the sampling points on the boundary in the image block partition. For example, when the dividing line used by the image block passes through the specified boundary (including the first boundary and/or the second boundary), the dividing line can be based on the location of the dividing line in the image block.
  • the boundary sampling point and the critical sampling point on the specified boundary determine the partition range.
  • the critical sampling point refers to the sampling point at the edge of the image block, such as the critical sampling point included in Figure 8b above.
  • the range value of the partition range along the specified boundary can be determined based on the different position coordinates of the boundary sampling point and the critical sampling point.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point, which may be adjacent to the image block partition or not adjacent to the image block partition.
  • a first reference sampling point and/or a second reference sampling point which may be adjacent to the image block partition or not adjacent to the image block partition.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point
  • the first reference sampling point is adjacent to the corresponding partition and not adjacent to another partition
  • the second reference sampling point is not adjacent to the corresponding partition and adjacent to the other partition;
  • the positional relationship of the first reference sampling point relative to the corresponding partition is different from the positional relationship of the second reference sampling point relative to the corresponding partition;
  • the first reference sampling point or the second reference sampling point is a pixel point adjacent to the coding tree unit where the target image block is located and not adjacent to the target image block.
  • S1002 Determine a prediction result set based on the target reference sampling point and the preset prediction mode.
  • a prediction result set of the image block may be determined based on the target reference sampling point and the preset prediction mode.
  • the prediction result set is used to determine the prediction result of the image block.
  • the prediction result set is used to determine the prediction result of the image block.
  • the prediction result set includes a first prediction result set and/or a second prediction result set.
  • the first prediction result set and/or the second prediction result set can be obtained according to the preset prediction modes used in different partitions of the image block.
  • the prediction result of the image block can be determined based on the first prediction result set and/or the second prediction result set.
  • the image processing solution provided by this embodiment determines the target reference sampling point through a preset strategy.
  • the prediction strategy may refer to whether the positional relationship between the reference sampling point and the image block partition sampling point meets the conditions. When the conditions are met, for example, the reference sampling point The point is adjacent to the corresponding partition of the image block, and is the target reference sampling point for this partition.
  • the target reference sampling points can be used to determine the reference sampling points that are available or unavailable for the image block partition, and in the preset prediction mode, an accurate prediction result set can be obtained based on the target reference sampling points, thereby improving the quality of the prediction results.
  • Figure 11 is a schematic flow chart of an image processing method according to the fifth embodiment.
  • the execution subject in this embodiment can be a computer device or a cluster composed of multiple computer devices.
  • the computer device can It may be an intelligent terminal (such as the aforementioned mobile terminal 100), or it may be a server.
  • the execution subject in this embodiment is an intelligent terminal as an example for explanation.
  • a preset prediction mode may be determined according to prediction mode indication information of an image block, and a prediction result set may be determined according to the preset prediction mode and the target reference sampling point.
  • the prediction mode indication information of the image block includes prediction mode indication information used to indicate the prediction mode used by the first partition, and/or prediction information used to indicate the prediction mode used by the second partition.
  • Mode indication information may be a tag or index of the prediction mode type used by the corresponding partition of the target image block at the encoding end, and may be used at the encoding end or the decoding end.
  • the preset prediction mode is: the prediction mode used by the partitions divided by the dividing lines in the image block and/or the prediction mode used by the adjacent image blocks.
  • the prediction modes used by the partitions divided by the dividing lines in the target image block include any of the following: prediction modes used by adjacent image blocks, and the prediction mode used by at least one adjacent image block has a usage number greater than or Prediction mode equal to preset threshold.
  • the prediction mode used by the adjacent image block may be used as the prediction mode used by the partition divided by the dividing line in the target image block.
  • the prediction mode that is used the most or more frequently among the prediction modes used by at least one adjacent image block may be used as the prediction mode used by the partitions divided by the dividing lines in the target image.
  • the prediction mode used by the partitions divided by the dividing lines in the target image can be obtained through other methods.
  • the preset prediction mode may also be determined by calculating the rate distortion cost.
  • the preset prediction mode may include the prediction mode used by the first partition and/or the prediction mode used by the second partition, and the prediction mode includes the third partition.
  • a prediction mode and/or a second prediction mode For example, if the image block partition includes a first partition and a second partition, then there is any of the following situations: both partitions use the first prediction mode; both partitions use Second prediction mode; one partition uses the first prediction mode and the other partition uses the second prediction mode.
  • the prediction mode used by the adjacent image block can be used as a reference for the image block currently being encoded. For example, if the adjacent image block uses the first prediction mode, the currently encoded image block can also directly use the first prediction mode.
  • the prediction mode is the prediction mode used by the partitions divided by the dividing lines in the image block.
  • the prediction mode is the prediction mode used by the partitions divided by the dividing lines in the image block.
  • the prediction mode includes the first prediction mode and/or the second prediction mode, determine a prediction result set corresponding to the partition of the image block based on the motion vector and/or the target reference sampling point.
  • the prediction mode includes the second prediction mode
  • a set of prediction results for the corresponding partition of the image block is determined according to the target reference sampling point.
  • the second prediction mode is an intra prediction mode.
  • the target reference sampling point includes a first reference sampling point and a second reference sampling point
  • determining a prediction result set corresponding to the partition of the image block based on the target reference sampling point includes: The second reference sampling point is filled to obtain a filled reference sampling point; and a prediction result set corresponding to the partition of the image block is determined based on the first reference sampling point and the filled reference sampling point.
  • the second reference sampling point is unavailable for the image block corresponding partition using the second prediction mode
  • the first reference sampling point is available for the image block corresponding partition using the second prediction mode
  • use the available reference sampling points to fill the unavailable reference sampling points, and determine the prediction result set corresponding to the partition of the image block based on the filled reference sampling points and the available reference sampling points, which can reduce the number of unavailable reference sampling points.
  • the filling method for the second reference sampling point may include: determining at least one filling reference sampling point in the first reference sampling point; determining the filling reference sampling point based on the sample value of the at least one filling reference sampling point. filling value of the second reference sampling point; filling the second reference sampling point based on the filling value to obtain a filled reference sampling point.
  • a filling weight can be introduced for different filling reference samples to determine the filling value: based on the relationship between each of the at least one filling reference sampling point and the The positional relationship between the second reference sampling points determines the filling weight of each filling reference sampling point; the filling value of the second reference sampling point is determined based on the filling weight and the sampling value of each filling reference sampling point.
  • the padding weight decreases with distance.
  • filling reference sampling points with strong correlation can be used to make the filling reference sampling points play a greater role in adjusting the difference between the second reference sampling point and the sampling points in the partition. , obtain better filling quality, thereby better reducing residual errors and improving coding quality.
  • the positional relationship includes the distance between each of the at least one filling reference sampling point and the second reference sampling point; the first reference sampling point includes the distance between the first reference sampling point and the target image block.
  • Determining the filling value of the second reference sampling point based on the filling weight and the sampling value of each filling reference sampling point includes: based on the sampling value of the first filling reference sampling point and the first filling The filling weight of the reference sampling point, and/or, the sampling value of the second filling reference sampling point and the filling weight of the second filling reference sampling point, determine the filling weight of the second reference sampling point adjacent to the corresponding boundary. Fill value.
  • the prediction mode includes the first prediction mode
  • a set of prediction results for the partition corresponding to the image block is determined according to the motion vector.
  • the motion vector includes a first running vector and/or a second motion vector.
  • the type of the first prediction mode includes a bidirectional prediction mode and/or a unidirectional prediction mode.
  • the prediction result set of the corresponding partition of the image block can be determined according to the first motion vector and the second motion vector
  • the prediction result set of the corresponding partition of the image block may be determined according to the first motion vector or the second motion vector.
  • the prediction mode includes a first prediction mode and a second prediction mode
  • a set of prediction results for the partition corresponding to the image block is determined based on the motion vector
  • a prediction result set for the partition corresponding to the image block is determined based on the target reference sampling point.
  • Result collection refers to the motion vector of the image block partition using the first prediction mode
  • the target reference sampling point is the reference sampling point corresponding to the image block partition using the second prediction mode.
  • the image block partition using the first prediction mode is different from the image block partition using the second prediction mode.
  • S1102 Determine the prediction result set of the image block according to the prediction result set of the corresponding partition of the image block.
  • the prediction result set of the corresponding partition of the image block includes a first prediction result set of the first partition and/or a second prediction result set of the second partition.
  • the first prediction result set may be obtained through the first prediction mode or the second prediction mode.
  • the second prediction result set may also be obtained through the first prediction mode or the second prediction mode.
  • the image block shown in FIG. 8c includes A partition and B partition, the first prediction mode is the inter prediction mode, and the second prediction mode is the intra prediction mode.
  • the process of determining the first prediction result set and the second prediction result set of the current block includes the following content: For using intra prediction partition, after determining the target reference sampling point of the partition, use the specifically used intra prediction mode type to determine the intra prediction value of the current block, and obtain the first prediction value set, which is a set of intra prediction values.
  • the predicted set of predicted values depending on the partition using the intra prediction mode, can use the first set of predicted values as the first set of prediction results or the second set of prediction results.
  • the first set of predicted values can be used as the first set of prediction results.
  • the inter prediction motion vector corresponding to the partition is obtained through the spatial domain merging candidate list, and then the inter prediction motion vector of the current block with respect to the inter prediction motion vector is determined. Predicted values, thereby obtaining a second set of predicted values.
  • the first set of predicted values is a set of predicted values related to intra prediction.
  • the process of determining the first prediction value set and the second prediction value set of the current block includes the following content: if both the A partition and the B partition perform intra prediction, determine respectively The respective reference sampling points and unavailable reference sampling points of partition A and partition B. After each unavailable reference sampling point is filled with the respective nearest available reference sampling point, the intra prediction mode type determined by each partition and the respective final reference sampling point are respectively used to determine the current block for each partition. The intra prediction value corresponding to the determined intra prediction mode type is used to obtain a first prediction result set and a second prediction result set.
  • the first prediction result set and the second prediction result set are both obtained through intra-frame prediction.
  • the first prediction result set and the second prediction result set may be prediction value sets related to intra-frame prediction.
  • these unavailable reference sampling points are Reference sampling assigns new sample values based on the sample values of available reference sample points, which can make the difference between the sample values of these reference sample points and the original values of the sample points in the partition where the second prediction (such as intra prediction) is performed is small, so the corresponding The resulting residuals are smaller and the coding quality is higher.
  • the types of second prediction modes (such as intra prediction modes) that can be combined with the division mode of image blocks (such as GPM mode) are expanded, thereby making prediction according to the second prediction mode in the division mode flexible. The accuracy and quality of prediction results are improved.
  • Figure 12 is a schematic structural diagram of an image processing device according to an embodiment of the present application.
  • the image processing device can be a computer program (including program code) running in a server.
  • the image processing device is an application.
  • Software this device can be used to execute corresponding steps in the method provided by the embodiments of this application.
  • the image processing device 1200 includes: a determination module 1201 and a filling module 1202.
  • the determination module 1201 is configured to determine a prediction result set of a corresponding partition in a target image block according to a preset prediction mode, and the prediction result set is used to determine a prediction result of the target image block.
  • the target image block includes a first partition and/or a second partition, and the first partition and/or the second partition are image areas divided by dividing lines;
  • the preset prediction mode includes the The prediction mode used by the partitions divided by the dividing lines in the target image block;
  • the prediction result set includes a first prediction result set of the first partition and/or a second prediction result set of the second partition.
  • the determination module 1201 is further configured to: determine a target division mode parameter of the target image block according to the first division mode set; the target division mode parameter includes a parameter used to indicate the corresponding partition in the target image block. Prediction mode indication information for the prediction mode.
  • the determination module 1201 is specifically configured to at least one of the following: if the prediction mode is the first prediction mode, determine the corresponding partition in the target image block according to the motion vector of the corresponding partition in the target image block. A set of prediction results of the partition; if the prediction mode is the second prediction mode, the set of prediction results of the corresponding partition in the target image block is determined according to the target reference sampling point of the corresponding partition in the target image block.
  • the determination module 1201 is specifically configured to: determine the first motion vector and/or the second motion vector of the corresponding partition in the target image block according to the merge candidate list of the target image block; A motion vector and/or the second motion vector determines a prediction result set of a corresponding partition in the target image block.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point.
  • the first reference sampling point and the second reference sampling point are different;
  • the first reference sampling point is adjacent to the corresponding partition and not adjacent to another partition
  • the second reference sampling point is not adjacent to the corresponding partition and adjacent to the other partition;
  • the first reference sampling point or the second reference sampling point is a pixel point adjacent to the coding tree unit where the target image block is located and not adjacent to the target image block.
  • the determination module 1201 is specifically configured to: determine the first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block; according to the first reference sampling point and/or the The second reference sampling point determines the prediction result set of the corresponding partition in the target image block.
  • the determination module 1201 is specifically configured to: based on the positional relationship between the corresponding partition in the target image block and the boundary sampling point of the dividing line used in the target image block in the target image block, Determine the first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block.
  • the determination module 1201 is specifically configured to at least one of the following: when the dividing line used by the target image block passes through the first boundary of the target image block, determine that the boundary sampling point includes the first boundary. Sampling point; when the dividing line used by the target image block passes through the second boundary of the target image block, it is determined that the boundary sampling point includes the second boundary sampling point.
  • the determination module 1201 is further configured to determine a dividing line equation according to the target division mode parameter of the target image block; and determine at least one boundary sampling point according to a boundary reference point and the dividing line equation.
  • the determination module 1201 is specifically configured to: determine the first coordinate range and/or the second coordinate range according to the at least one boundary sampling point; The range determines the first reference sampling point and/or the second reference sampling point.
  • the determination module 1201 is specifically configured to: determine the partition range of the corresponding partition in the target image block based on the distance between the sampling point of the corresponding partition in the target image block and the dividing line; based on the first The coordinate range of the sampling point and the partition range determine the first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block; or, based on the first sampling point adjacent to the target image block The distance information from the dividing line determines that the first sampling point is the first reference sampling point or the second reference sampling point of the corresponding partition in the target image block.
  • the filling module 1202 is configured to: fill the second reference sampling point according to the first reference sampling point to obtain a filled reference sampling point; according to the first reference sampling point and the The filled reference sampling points determine the prediction result set of the corresponding partition of the target image block.
  • the filling value of the second reference sampling point is determined based on the sampling value of the at least one filling reference sampling point; the second reference sampling point is filled based on the filling value to obtain a filled reference sampling point.
  • the first reference sampling point includes a first reference sampling point adjacent to a first boundary of the target image block, and/or a first reference sampling point adjacent to a second boundary of the target image block;
  • the at least one filling reference sampling point includes a first filling reference sampling point among the first reference sampling points adjacent to the first boundary, and/or one of the first reference sampling points adjacent to the second boundary.
  • the filling module 1202 is specifically configured to: based on the sampling value of the first filling reference sampling point and the filling weight of the first filling reference sampling point, and/or, the sampling value of the second filling reference sampling point and the filling weight of the first filling reference sampling point.
  • the filling weight of the second filling reference sampling point determines the filling value of the second reference sampling point adjacent to the corresponding boundary.
  • the image processing device 1200 shown in FIG. 12 can also be used for the content included in the following image processing method.
  • Determination module 1201 used to determine the target reference sampling point through a preset strategy
  • the determination module 1201 is also used to determine a prediction result set according to the target reference sampling point and the preset prediction mode.
  • the determination module 1201 is specifically configured to determine the target reference sampling point through the positional relationship between the reference sampling point and the sampling point in the image block partition.
  • the determination module 1201 is specifically configured to: determine at least one boundary sampling point from the sampling points of the image block partition; determine the target reference sample according to the positional relationship between the reference sampling point and the at least one boundary sampling point. point.
  • the at least one boundary sampling point includes a first boundary sampling point and/or a second boundary sampling point
  • the determination module 1201 is specifically configured to: determine a first coordinate range according to the first boundary sampling point, and/ Or, determine the second coordinate range according to the second boundary sampling point;
  • the target reference sampling point is determined according to the positional relationship between the reference sampling point and the first coordinate range, and/or according to the positional relationship between the reference sampling point and the second coordinate range.
  • the determination module 1201 is specifically configured to: determine the partition range according to the sampling points in the image block partition; and determine the target reference sampling point according to the positional relationship between the reference sampling point and the partition range.
  • the determination module 1201 is specifically configured to: determine a preset prediction mode according to the prediction mode indication information of the image block; and determine a prediction result set according to the preset prediction mode and the target reference sampling point.
  • the preset prediction mode is at least one of the following: a prediction mode used by partitions divided by dividing lines in the image block and/or a prediction mode used by adjacent image blocks.
  • the determination module 1201 is specifically configured to: if the prediction mode includes a first prediction mode and/or a second prediction mode, determine the partition corresponding to the image block according to the motion vector and/or the target reference sampling point.
  • Prediction result set determine the prediction result set of the image block according to the prediction result set of the corresponding partition of the image block.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point
  • the first reference sampling point is adjacent to the corresponding partition and not adjacent to another partition
  • the second reference sampling point is not adjacent to the corresponding partition and adjacent to the other partition;
  • the positional relationship of the first reference sampling point relative to the corresponding partition is different from the positional relationship of the second reference sampling point relative to the corresponding partition;
  • the first reference sampling point or the second reference sampling point is a pixel point adjacent to the coding tree unit where the target image block is located and not adjacent to the target image block.
  • the filling module 1202 is specifically configured to: if the prediction mode includes a second prediction mode, fill the second reference sampling point according to the first reference sampling point to obtain a filled reference Sampling point; determine the image block according to the first reference sampling point and the filled reference sampling point The set of prediction results corresponding to the partition.
  • An embodiment of the present application also provides an intelligent terminal.
  • the intelligent terminal includes a memory and a processor.
  • An image processing program is stored on the memory. When the image processing program is executed by the processor, the steps of the image processing method in any of the above embodiments are implemented.
  • the smart terminal may be the mobile terminal 100 as shown in FIG. 1 .
  • the processor 110 of the mobile terminal 100 as shown in FIG. 1 can be used to call the image processing program stored in the memory 109 to perform the following operations: determine the corresponding target image block in the target image block according to the preset prediction mode. A set of prediction results of the partition, the set of prediction results is used to determine the prediction result of the target image block.
  • the target image block includes a first partition and/or a second partition, and the first partition and/or the second partition are image areas divided by dividing lines;
  • the preset prediction mode includes the The prediction mode used by the partitions divided by the dividing lines in the target image block;
  • the prediction result set includes a first prediction result set of the first partition and/or a second prediction result set of the second partition.
  • the processor 110 is further configured to: determine a target division mode parameter of the target image block according to the first division mode set; the target division mode parameter includes a parameter used to indicate the corresponding partition in the target image block. Prediction mode indication information for the prediction mode.
  • the processor 110 is specifically configured to at least one of the following: if the prediction mode is the first prediction mode, determine the corresponding partition in the target image block according to the motion vector of the corresponding partition in the target image block. A set of prediction results of the partition; if the prediction mode is the second prediction mode, the set of prediction results of the corresponding partition in the target image block is determined according to the target reference sampling point of the corresponding partition in the target image block.
  • the processor 110 is specifically configured to: determine the first motion vector and/or the second motion vector of the corresponding partition in the target image block according to the merge candidate list of the target image block; A motion vector and/or the second motion vector determines a prediction result set of a corresponding partition in the target image block.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point.
  • the first reference sampling point and the second reference sampling point are different;
  • the first reference sampling point is adjacent to the corresponding partition and not adjacent to another partition
  • the second reference sampling point is not adjacent to the corresponding partition and adjacent to the other partition;
  • the positional relationship of the first reference sampling point relative to the corresponding partition is different from the positional relationship of the second reference sampling point relative to the corresponding partition;
  • the first reference sampling point or the second reference sampling point is a pixel point adjacent to the coding tree unit where the target image block is located and not adjacent to the target image block.
  • the second prediction mode includes at least one type of second prediction mode. If the prediction mode is the second prediction mode, the processor 110 is further configured to: select the second prediction mode from the at least one type of second prediction mode. Among the two prediction modes, a second prediction mode of a target type used in a partition corresponding to the target image block is determined, and the second prediction mode of the target type is used to determine a prediction result set of the target image block.
  • the processor 110 is specifically configured to: determine the first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block; according to the first reference sampling point and/or the The second reference sampling point determines the prediction result set of the corresponding partition in the target image block.
  • the processor 110 is specifically configured to: based on the positional relationship between the corresponding partition in the target image block and the boundary sampling point of the dividing line used in the target image block in the target image block, Determine the first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block.
  • the processor 110 is specifically configured to: determine the boundary through which the dividing line used by the target image block passes according to the boundary mapping table; determine the boundary sampling point according to the boundary through which the dividing line passes.
  • the processor 110 is specifically configured to at least one of the following: when the dividing line used by the target image block passes through the first boundary of the target image block, determine that the boundary sampling point includes the first boundary Sampling point; when the dividing line used by the target image block passes through the second boundary of the target image block, it is determined that the boundary sampling point includes the second boundary sampling point.
  • the processor 110 is further configured to determine a dividing line equation according to a target division mode parameter of the target image block; and determine at least one boundary sampling point according to a boundary reference point and the dividing line equation.
  • the processor 110 is specifically configured to: determine a first coordinate range and/or a second coordinate range according to the at least one boundary sampling point; The range determines the first reference sampling point and/or the second reference sampling point.
  • the processor 110 is specifically configured to: determine the partition range of the corresponding partition in the target image block based on the distance between the sampling point of the corresponding partition in the target image block and the dividing line; based on the first The coordinate range of the sampling point and the partition range determine the first reference sampling point and/or the second reference sampling point of the corresponding partition in the target image block; or, based on the first sampling point adjacent to the target image block The distance information from the dividing line determines that the first sampling point is the first reference sampling point or the second reference sampling point of the corresponding partition in the target image block.
  • the processor 110 is configured to: fill the second reference sampling point according to the first reference sampling point to obtain a filled reference sampling point; according to the first reference sampling point and the The filled reference sampling points determine the prediction result set of the corresponding partition of the target image block.
  • the processor 110 is specifically configured to: determine at least one filling reference sampling point in the first reference sampling point;
  • the filling value of the second reference sampling point is determined based on the sampling value of the at least one filling reference sampling point; the second reference sampling point is filled based on the filling value to obtain a filled reference sampling point.
  • the processor 110 is specifically configured to: determine the filling weight of each filling reference sampling point based on the positional relationship between each filling reference sampling point and the second reference sampling point; based on the filling weight and the sampling values of each filling reference sampling point to determine the filling value of the second reference sampling point.
  • the positional relationship includes the distance between each of the filling reference sampling points and the second reference sampling point
  • the first reference sampling point includes a first reference sampling point adjacent to a first boundary of the target image block, and/or a first reference sampling point adjacent to a second boundary of the target image block;
  • the at least one filling reference sampling point includes a first filling reference sampling point among the first reference sampling points adjacent to the first boundary, and/or one of the first reference sampling points adjacent to the second boundary.
  • the processor 110 is specifically configured to: based on the sampling value of the first filling reference sampling point and the filling weight of the first filling reference sampling point, and/or, the sampling value of the second filling reference sampling point and the filling weight of the first filling reference sampling point.
  • the filling weight of the second filling reference sampling point determines the filling value of the second reference sampling point adjacent to the corresponding boundary.
  • the processor 110 of the mobile terminal 100 as shown in FIG. 1 can be used to call an image processing program stored in the memory 109 to perform the following operations: determine the target reference sampling through a preset strategy point; determine a prediction result set according to the target reference sampling point and the preset prediction mode.
  • the processor 110 is specifically configured to: determine the target reference sampling point through the positional relationship between the reference sampling point and the sampling point in the image block partition.
  • the processor 110 is specifically configured to: determine at least one boundary sampling point from the sampling points of the image block partition; determine the target reference sample according to the positional relationship between the reference sampling point and the at least one boundary sampling point. point.
  • the at least one boundary sampling point includes a first boundary sampling point and/or a second boundary sampling point
  • the processor 110 is specifically configured to: determine a first coordinate range according to the first boundary sampling point, and/ Or, determine the second coordinate range according to the second boundary sampling point;
  • the target reference sampling point is determined according to the positional relationship between the reference sampling point and the first coordinate range, and/or according to the positional relationship between the reference sampling point and the second coordinate range.
  • the processor 110 is specifically configured to: determine the partition range according to the sampling points in the image block partition; and determine the target reference sampling point according to the positional relationship between the reference sampling point and the partition range.
  • the processor 110 is specifically configured to: determine a preset prediction mode according to the prediction mode indication information of the image block; and determine a prediction result set according to the preset prediction mode and the target reference sampling point.
  • the preset prediction mode is at least one of the following: a prediction mode used by partitions divided by dividing lines in the image block and/or a prediction mode used by adjacent image blocks.
  • the processor 110 is specifically configured to: if the prediction mode includes a first prediction mode and/or a second prediction mode, determine the partition corresponding to the image block according to the motion vector and/or the target reference sampling point.
  • Prediction result set determine the prediction result set of the image block according to the prediction result set of the corresponding partition of the image block.
  • the target reference sampling point includes a first reference sampling point and/or a second reference sampling point
  • the first reference sampling point is adjacent to the corresponding partition and not adjacent to another partition
  • the positional relationship of the first reference sampling point relative to the corresponding partition is different from the positional relationship of the second reference sampling point relative to the corresponding partition;
  • the first reference sampling point or the second reference sampling point is a pixel point adjacent to the coding tree unit where the target image block is located and not adjacent to the target image block.
  • the processor 110 is specifically configured to: if the prediction mode includes a second prediction mode, fill the second reference sampling point according to the first reference sampling point to obtain a filled reference Sampling points; determine a prediction result set corresponding to the partition of the image block based on the first reference sampling point and the filled reference sampling point.
  • the mobile terminal described in the embodiments of this application can execute the method description in any of the above embodiments, and can also execute the description of the image processing device in the above corresponding embodiment, which will not be described again here. In addition, the description of the beneficial effects of using the same method will not be described again.
  • Embodiments of the present application also provide a computer-readable storage medium.
  • An image processing program is stored on the storage medium.
  • the image processing program is executed by a processor, the steps of the image processing method in any of the above embodiments are implemented.
  • FIG 13 is a schematic diagram of the hardware structure of a controller according to an embodiment of the present application.
  • the controller 140 includes: a memory 1401 and a processor 1402.
  • the memory 1401 is used to store program instructions.
  • the processor 1402 is used to call the program instructions in the memory 1401 to execute the steps performed by the controller in the above method embodiment. Its implementation principle and The beneficial effects are similar and will not be described again here.
  • the above-mentioned controller also includes a communication interface 1403, which can be connected to the processor 1402 through a bus 1404.
  • the processor 1402 can control the communication interface 1403 to implement the receiving and sending functions of the controller 140.
  • FIG 14 is a schematic diagram of the hardware structure of a network node according to an embodiment of the present application.
  • the network node 150 includes: a memory 1501 and a processor 1502.
  • the memory 1501 is used to store program instructions.
  • the processor 1502 is used to call the program instructions in the memory 1501 to execute the steps performed by the first node in the above method embodiment. Its implementation principle and The beneficial effects are similar and will not be described again here.
  • the above-mentioned network node also includes a communication interface 1503, which can be connected to the processor 1502 through a bus 1504.
  • the processor 1502 can control the communication interface 1503 to implement the receiving and transmitting functions of the network node 150 .
  • Embodiments of the present application also provide a computer program product.
  • the computer program product includes computer program code.
  • the computer program code When the computer program code is run on a computer, it causes the computer to execute the methods in the above various possible implementations.
  • Embodiments of the present application also provide a chip, which includes a memory and a processor.
  • the memory is used to store a computer program.
  • the processor is used to call and run the computer program from the memory, so that the device equipped with the chip executes the above various possible implementations. Methods.
  • the units in the equipment of the embodiments of this application can be merged, divided, and deleted according to actual needs.
  • the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation.
  • the technical solution of the present application can be embodied in the form of a software product in essence or that contributes to the existing technology.
  • the computer software product is stored in one of the above storage media (such as ROM/RAM, magnetic disk, optical disk), including several instructions to cause 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.
  • a computer program product includes one or more computer instructions.
  • Computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g., computer instructions may be transmitted from a website, computer, server or data center via a wired link (e.g.
  • Coaxial cable, optical fiber, digital subscriber line) or wireless means to transmit to another website, computer, server or data center.
  • Computer-readable storage media can be any available media that can be accessed by a computer or a data storage device such as a server, data center, or other integrated media that contains one or more available media. Available media may be magnetic media (eg, floppy disks, storage disks, tapes), optical media (eg, DVD), or semiconductor media (eg, Solid State Disk (SSD)), etc.
  • the above integrated modules implemented in the form of software function modules can be stored in a computer-readable storage medium.
  • the above-mentioned software function modules are stored in a storage medium and include a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to execute the methods of various embodiments of the present application. Some steps.

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Abstract

本申请公开了一种图像处理方法、智能终端及存储介质，该图像处理方法包括以下步骤：根据预设预测模式确定目标图像块中对应分区的预测结果集合，所述预测结果集合用于确定所述目标图像块的预测结果。通过本申请实施例，可以灵活地使用预设预测模式进行预测，并提高预测结果的准确性。

Description

图像处理方法、智能终端及存储介质
本申请要求于2022年05月07日提交中国专利局、申请号为202210491875.0、申请名称为“图像处理方法、智能终端及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及图像处理技术领域，具体涉及一种图像处理方法、智能终端及存储介质。
背景技术
在视频编解码的过程中，使用预测模式对图像块进行预测是十分重要的环节。通过相应的预测模式对图像块进行预测可以有效去除视频时域或者空域的冗余，从而将视频压缩以更好地进行传输。
在构思及实现本申请过程中，发明人发现至少存在如下问题：图像块中对应分区使用预测模式的灵活性和预测结果的准确性之间不能很好地达到平衡。
前面的叙述在于提供一般的背景信息，并不一定构成现有技术。
申请内容
针对上述技术问题，本申请提供一种图像处理方法、智能终端及存储介质，可以灵活地使用预设预测模式进行预测，并提高预测结果的准确性。
为解决上述技术问题，本申请提供一种图像处理方法，可应用于智能终端，包括：
根据预设预测模式确定目标图像块的预测结果集合，预测结果集合用于确定目标图像块的预测结果。
可选地，所述目标图像块包括第一分区和/或第二分区，所述第一分区和/或第二分区是由分割线划分得到的图像区域；所述预设预测模式包括所述目标图像块中分割线划分的分区所使用的预测模式；所述预测结果集合包括第一预测结果集合和/或第二预测结果集合。
可选地，该方法还包括：根据第一划分模式集合确定目标图像块的目标划分模式参数；所述目标划分模式参数包括用于指示所述目标图像块中对应分区所使用的预测模式的预测模式指示信息。
可选地，所述根据预设预测模式确定目标图像块中对应分区的预测结果集合，包括以下至少一项：若所述预测模式为第一预测模式，则根据所述目标图像块中对应分区的运动矢量确定所述目标图像块中对应分区的预测结果集合；若所述预测模式为第二预测模式，则根据所述目标图像块中对应分区的目标参考采样点确定所述目标图像块中对应分区的预测结果集合。
可选地，所述根据所述目标图像块中对应分区的运动矢量确定所述目标图像块中对应分区的预测结果集合，包括：根据所述目标图像块的合并候选列表和/或目标参考采样点确定所述目标图像块中对应分区的第一运动矢量和/或第二运动矢量；根据所述第一运动矢量和/或所述第二运动矢量确定所述目标图像块中对应分区的预测结果集合。
可选地，所述目标参考采样点包括第一参考采样点和/或第二参考采样点。
可选地，所述方法还包括以下至少一项：
所述第一参考采样点和所述第二参考采样点不同；
所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
可选地，所述第二预测模式包括至少一种类型的第二预测模式，若所述预测模式为第二预测模式，则所述方法还包括：从所述至少一种类型的第二预测模式中确定所述目标图像块对应分区所使用的目标类型的第二预测模式，所述目标类型的第二预测模式用于确定所述目标图像块的预测结果集合。
可选地，所述根据所述目标图像块中对应分区的目标参考采样点确定所述目标图像块中对应分区的预测结果集合，包括以下步骤：S21：确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；S22：根据所述第一参考采样点和/或所述第二参考采样点确定所述目标图像块中对应分区的预测结果集合。
可选地，所述步骤S21包括：根据所述目标图像块中对应分区与所述目标图像块使用的分割线在所述目标图像块中的边界采样点之间的位置关系，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点。
可选地，所述方法还包括：根据边界映射表确定所述目标图像块使用的分割线所经过的边界；根据所述分割线所经过的边界确定所述边界采样点。
可选地，所述根据所述分割线所经过的边界确定所述边界采样点，包括以下至少一项：当所述目标图像块使用的分割线经过所述目标图像块的第一边界时，确定所述边界采样点包括第一边界采样点；当所述目标图像块使用的分割线经过所述目标图像块的第二边界时，确定所述边界采样点包括第二边界采样点。
可选地，所述边界采样点的确定方式，包括：根据所述目标图像块的目标划分模式参数确定分割线方程；根据边界参考点和所述分割线方程确定至少一所述边界采样点。
可选地，所述根据所述目标图像块中对应分区与所述目标图像块使用的分割线在所述目标图像块中的边界采样点之间的位置关系，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点，包括：根据所述至少一边界采样点确定第一坐标范围和/或第二坐标范围；根据所述第一坐标范围和/或所述第二坐标范围确定第一参考采样点和/或第二参考采样点。
可选地，所述步骤S21包括：根据所述目标图像块中对应分区的采样点与分割线之间的距离，确定所述目标图像块中对应分区的分区范围；基于第一采样点的坐标范围和所述分区范围确定所述目标图像块中对应分区的第一参考采样点和/或第 二参考采样点；或者，基于与所述目标图像块相邻的第一采样点与所述分割线之间的距离信息，确定所述第一采样点为所述目标图像块中对应分区的第一参考采样点或者第二参考采样点。
可选地，所述步骤S22包括：根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；根据所述第一参考采样点和所述填充后的参考采样点确定所述目标图像块对应分区的预测结果集合。
可选地，所述根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点，包括：在所述第一参考采样点中确定至少一个填充参考采样点；基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值；基于所述填充值对所述第二参考采样点进行填充，得到填充后的参考采样点。
可选地，所述基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值，包括：基于各个填充参考采样点与所述第二参考采样点之间的位置关系确定所述各个填充参考采样点的填充权重；基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值。
可选地，包括以下至少一项：所述位置关系包括所述各个填充参考采样点和所述第二参考采样点之间的距离；所述第一参考采样点包括与所述目标图像块的第一边界相邻的第一参考采样点，和/或与所述目标图像块的第二边界相邻的第一参考采样点；所述至少一个填充参考采样点包括与所述第一边界相邻的第一参考采样点中的第一填充参考采样点，和/或与所述第二边界相邻的第一参考采样点中的第二填充参考采样点。
可选地，所述基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值，包括：基于第一填充参考采样点的采样值和第一填充参考采样点的填充权重，和/或，第二填充参考采样点的采样值和第二填充参考采样点的填充权重，确定与对应边界相邻的所述第二参考采样点的填充值。
本申请提供另一种图像处理方法，可应用于智能终端，包括以下步骤：
S1：通过预设策略确定目标参考采样点；
S2：根据所述目标参考采样点和预设预测模式，确定预测结果集合。
可选地，通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点。
可选地，所述S1步骤，包括：通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点。
可选地，所述通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点，包括：从图像块分区的采样点中确定至少一边界采样点；根据参考采样点与所述至少一边界采样点之间的位置关系确定目标参考采样点。
可选地，所述步骤S2包括：根据图像块的预测模式指示信息确定预设预测模式；根据所述预设预测模式和所述目标参考采样点，确定预测结果集合。
可选地，所述预设预测模式为以下至少一项：图像块中分割线划分的分区所使用的预测模式和/或相邻图像块所使用的预测模式。
可选地，所述预设预测模式为图像块中分割线划分的分区所使用的预测模式；所述根据所述预设预测模式和所述目标参考采样点，确定预测结果集合，包括：若所述预测模式包括第一预测模式和/或第二预测模式，则根据运动矢量和/或目标参考采样点，确定图像块对应分区的预测结果集合；根据所述图像块对应分区的预测结果集合确定所述图像块的预测结果集合。
可选地，若所述预测模式包括第二预测模式，则根据所述目标参考采样点确定图像块对应分区的预测结果集合，包括：
若所述预测模式包括第二预测模式，则根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；
根据所述第一参考采样点和所述填充后的参考采样点确定所述图像块对应分区的预测结果集合。
本申请还提供一种智能终端，包括：存储器、处理器，其中，存储器上存储有图像处理程序，图像处理程序被处理器执行时实现如上述任一图像处理方法的步骤。
本申请还提供一种计算机存储介质，计算机存储介质存储有计算机程序，计算机程序被处理器执行时实现如上述任一图像处理方法的步骤。
如上，本申请的图像处理方法，可应用于智能终端，包括：根据预设预测模式确定目标图像块的预测结果集合，预测结果集合用于确定目标图像块的预测结果。通过上述技术方案，预设预测模式可以是预设的任意类型的预测模式，预设预测模式的选择范围并不受限制，使用灵活性高；根据预设预测模式可以确定较为准确的目标图像块中对应分区的预测结果集合，进而可以根据预测结果集合确定出偏差较小的目标图像块的预测结果，提高预测结果的准确度。可见，采用本方案可以实现灵活使用预设预测模式，提高预测结果的准确度的功能，解决预测模式的使用灵活性与预测结果的准确度之间不平衡的问题。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本申请的实施例，并与说明书一起用于解释本申请的原理。为了更清楚地说明本申请实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为实现本申请各个实施例的一种移动终端的硬件结构示意图；
图2为本申请实施例提供的一种通信网络系统架构图；
图3是根据第一实施例示出的图像处理方法的流程示意图；
图4a是根据第一实施例示出的一种分割线划分图像块的效果示意图；
图4b是根据第一实施例示出的一种角度划分的示意图；
图4c是根据第一实施例示出的一种在角度φi下对应的多种偏移的示意图；
图4d是根据第一实施例示出的一种划分模式的示意图；
图5是根据第二实施例示出的图像处理方法的流程示意图；
图6a是根据第二实施例示出的一种示例性地空域合并候选列表的相邻块位置示意图；
图6b是根据第二实施例示出的一种示例性地合并候选列表的示意图；
图6c是根据第二实施例示出的一种示例性地目标参考采样点的示意图；
图6d是根据第二实施例示出的一种目标图像块的编码示意图；
图6e是根据第二实施例示出的一种像素点和分割线的距离分析的示意图；
图7是根据第三实施例示出的图像处理方法的流程示意图；
图8a是根据第三实施例示出的目标图像块中分割线经过边界的情况；
图8b是根据第三实施例示出的边界参考点和边界采样点之间的位置关系示意图；
图8c是根据第三实施例示出的一种目标参考采样点划分的示意图；
图8d是根据第三实施例示出的另一种目标参考采样点划分示意图；
图9a和图9b是根据第三实施例示出的一些填充参考采样点的示意图；
图9c是根据第三实施例示出的一种使用填充参考采样点的示意图；
图9d是根据第三实施例示出的另一种使用填充参考采样点的示意图；
图9e是根据第三实施例示出的又一种使用填充参考采样点的示意图；
图9f是根据第三实施例示出的广角帧内预测模式的示意图；
图10是根据第四实施例示出的图像处理方法的流程示意图；
图11是根据第五实施例示出的图像处理方法的流程示意图；
图12是本申请实施例示出的一种图像处理装置的结构示意图；
图13是本申请实施例示出的一种控制器的硬件结构示意图；
图14是本申请实施例示出的一种网络节点的硬件结构示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。通过上述附图，已示出本申请明确的实施例，后文中将有更详细的描述。这些附图和文字描述并不是为了通过任何方式限制本申请构思的范围，而是通过参考特定实施例为本领域技术人员说明本申请的概念。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本申请的一些方面相一致的装置和方法的例子。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素，此外，本申请不同实施例中具有同样命名的部件、特征、要素可能具有相同含义，也可能具有不同含义，其具体含义需以其在该具体实施例中的解释或者进一步结合该具体实施例中上下文进行确定。
应当理解，尽管在本文可能采用术语第一、第二、第三等来描述各种信息，但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如，在不脱离本文范围的情况下，第一信息也可以被称为第二信息，类似地，第二信息也可以被称为第一信息。取决于语境，如在此所使用的词语"如果"可以被解释成为"在……时"或"当……时"或"响应于确定"。再者，如同在本文中所使用的，单数形式“一”、“一个”和“该”旨在也包括复数形式，除非上下文中有相反的指示。应当进一步理解，术语“包含”、“包括”表明存在的特征、步骤、操作、元件、组件、项目、种类、和/或组，但不排除一个或多个其他特征、步骤、操作、元件、组件、项目、种类、和/或组的存在、出现或添加。本申请使用的术语“或”、“和/或”、“包括以下至少一个”等可被解释为包括性的，或意味着任一个或任何组合。例如，“包括以下至少一个：A、B、C”意味着“以下任一个：A；B；C；A和B；A和C；B和C；A和B和C”，再如，“A、B或C”或者“A、B和/或C”意味着“以下任一个：A；B；C；A和B；A和C；B和C；A和B和C”。仅当元件、功能、步骤或操作的组合在某些方式下内在地互相排斥时，才会出现该定义的例外。
应该理解的是，虽然本申请实施例中的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，其可以以其他的顺序执行。而且，图中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，其执行顺序也不必然是依次进行，而是可以与其他步骤或者其他步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
取决于语境，如在此所使用的词语“如果”、“若”可以被解释成为“在……时”或“当……时”或“响应于确定”或“响应于检测”。类似地，取决于语境，短语“如果确定”或“如果检测(陈述的条件或事件)”可以被解释成为“当确定时”或“响应于确定”或“当检测(陈述的条件或事件)时”或“响应于检测(陈述的条件或事件)”。
需要说明的是，在本文中，采用了诸如S501、S502等步骤代号，其目的是为了更清楚简要地表述相应内容，不构成顺序上的实质性限制，本领域技术人员在具体实施时，可能会先执行S502后执行S501等，但这些均应在本申请的保护范围之内。
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
在后续的描述中，使用用于表示元件的诸如“模块”、“部件”或者“单元”的后缀仅为了有利于本申请的说明，其本身没有特定的意义。因此，“模块”、“部件”或者“单元”可以混合地使用。
智能终端可以以各种形式来实施。例如，本申请中描述的智能终端可以包括诸如手机、平板电脑、笔记本电脑、掌上电脑、个人数字助理(Personal Digital Assistant，PDA)、便捷式媒体播放器(Portable Media Player，PMP)、导航装置、可穿戴设备、智能手环、计步器等智能终端，以及诸如数字TV、台式计算机等固定终端。
后续描述中将以移动终端为例进行说明，本领域技术人员将理解的是，除了特别用于移动目的的元件之外，根据本申请的实施方式的构造也能够应用于固定类型的终端。
请参阅图1，其为实现本申请各个实施例的一种移动终端的硬件结构示意图，该移动终端100可以包括：RF(Radio Frequency，射频)单元101、WiFi模块102、音频输出单元103、A/V(音频/视频)输入单元104、传感器105、显示单元106、用户输入单元107、接口单元108、存储器109、处理器110、以及电源111等部件。本领域技术人员可以理解，图1中示出的移动终端结构并不构成对移动终端的限定，移动终端可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
下面结合图1对移动终端的各个部件进行具体的介绍：
射频单元101可用于收发信息或通话过程中，信号的接收和发送，具体的，将基站的下行信息接收后，给处理器110处理；另外，将上行的数据发送给基站。通常，射频单元101包括但不限于天线、至少一个放大器、收发信机、耦合器、低噪声放大器、双工器等。此外，射频单元101还可以通过无线通信与网络和其他设备通信。上述无线通信可以使用任一通信标准或协议，包括但不限于GSM(Global System of Mobile communication，全球移动通讯系统)、GPRS(General Packet Radio Service，通用分组无线服务)、CDMA2000(Code Division Multiple Access 2000，码分多址2000)、WCDMA(Wideband Code  Division Multiple Access,宽带码分多址)、TD-SCDMA(Time Division-Synchronous Code Division Multiple Access，时分同步码分多址)、FDD-LTE(Frequency Division Duplexing-Long Term Evolution，频分双工长期演进)、TDD-LTE(Time Division Duplexing-Long Term Evolution，分时双工长期演进)和5G等。
WiFi属于短距离无线传输技术，移动终端通过WiFi模块102可以帮助用户收发电子邮件、浏览网页和访问流式媒体等，它为用户提供了无线的宽带互联网访问。虽然图1示出了WiFi模块102，但是可以理解的是，其并不属于移动终端的必须构成，完全可以根据需要在不改变发明的本质的范围内而省略。
音频输出单元103可以在移动终端100处于呼叫信号接收模式、通话模式、记录模式、语音识别模式、广播接收模式等等模式下时，将射频单元101或WiFi模块102接收的或者在存储器109中存储的音频数据转换成音频信号并且输出为声音。而且，音频输出单元103还可以提供与移动终端100执行的特定功能相关的音频输出(例如，呼叫信号接收声音、消息接收声音等等)。音频输出单元103可以包括扬声器、蜂鸣器等等。
A/V输入单元104用于接收音频或视频信号。A/V输入单元104可以包括图形处理器(Graphics Processing Unit，GPU)1041和麦克风1042，图形处理器1041对在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。处理后的图像帧可以显示在显示单元106上。经图形处理器1041处理后的图像帧可以存储在存储器109(或其它存储介质)中或者经由射频单元101或WiFi模块102进行发送。麦克风1042可以在电话通话模式、记录模式、语音识别模式等等运行模式中经由麦克风1042接收声音(音频数据)，并且能够将这样的声音处理为音频数据。处理后的音频(语音)数据可以在电话通话模式的情况下转换为可经由射频单元101发送到移动通信基站的格式输出。麦克风1042可以实施各种类型的噪声消除(或抑制)算法以消除(或抑制)在接收和发送音频信号的过程中产生的噪声或者干扰。
移动终端100还包括至少一种传感器105，比如光传感器、运动传感器以及其他传感器。可选地，光传感器包括环境光传感器及接近传感器，可选地，环境光传感器可根据环境光线的明暗来调节显示面板1061的亮度，接近传感器可在移动终端100移动到耳边时，关闭显示面板1061和/或背光。作为运动传感器的一种，加速计传感器可检测各个方向上(一般为三轴)加速度的大小，静止时可检测出重力的大小及方向，可用于识别手机姿态的应用(比如横竖屏切换、相关游戏、磁力计姿态校准)、振动识别相关功能(比如计步器、敲击)等；至于手机还可配置的指纹传感器、压力传感器、虹膜传感器、分子传感器、陀螺仪、气压计、湿度计、温度计、红外线传感器等其他传感器，在此不再赘述。
显示单元106用于显示由用户输入的信息或提供给用户的信息。显示单元106可包括显示面板1061，可以采用液晶显示器(Liquid Crystal Display，LCD)、有机发光二极管(Organic Light-Emitting Diode,OLED)等形式来配置显示面板1061。
用户输入单元107可用于接收输入的数字或字符信息，以及产生与移动终端的用户设置以及功能控制有关的键信号输入。可选地，用户输入单元107可包括触控面板1071以及其他输入设备1072。触控面板1071，也称为触摸屏，可收集用户在其上或附近的触摸操作(比如用户使用手指、触笔等任何适合的物体或附件在触控面板1071上或在触控面板1071附近的操作)，并根据预先设定的程式驱动相应的连接装置。触控面板1071可包括触摸检测装置和触摸控制器两个部分。可选地，触摸检测装置检测用户的触摸方位，并检测触摸操作带来的信号，将信号传送给触摸控制器；触摸控制器从触摸检测装置上接收触摸信息，并将它转换成触点坐标，再送给处理器110，并能接收处理器110发来的命令并加以执行。此外，可以采用电阻式、电容式、红外线以及表面声波等多种类型实现触控面板1071。除了触控面板1071，用户输入单元107还可以包括其他输入设备1072。可选地，其他输入设备1072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆等中的一种或多种，具体此处不做限定。
可选地，触控面板1071可覆盖显示面板1061，当触控面板1071检测到在其上或附近的触摸操作后，传送给处理器110以确定触摸事件的类型，随后处理器110根据触摸事件的类型在显示面板1061上提供相应的视觉输出。虽然在图1中，触控面板1071与显示面板1061是作为两个独立的部件来实现移动终端的输入和输出功能，但是在某些实施例中，可以将触控面板1071与显示面板1061集成而实现移动终端的输入和输出功能，具体此处不做限定。
接口单元108用作至少一个外部装置与移动终端100连接可以通过的接口。例如，外部装置可以包括有线或无线头戴式耳机端口、外部电源(或电池充电器)端口、有线或无线数据端口、存储卡端口、用于连接具有识别模块的装置的端口、音频输入/输出(I/O)端口、视频I/O端口、耳机端口等等。接口单元108可以用于接收来自外部装置的输入(例如，数据信息、电力等等)并且将接收到的输入传输到移动终端100内的一个或多个元件或者可以用于在移动终端100和外部装置之间传输数据。
存储器109可用于存储软件程序以及各种数据。存储器109可主要包括存储程序区和存储数据区，可选地，存储程序区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据手机的使用所创建的数据(比如音频数据、电话本等)等。此外，存储器109可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
处理器110是移动终端的控制中心，利用各种接口和线路连接整个移动终端的各个部分，通过运行或执行存储在存储器109内的软件程序和/或模块，以及调用存储在存储器109内的数据，执行移动终端的各种功能和处理数据，从而对移动终端进行整体监控。处理器110可包括一个或多个处理单元；优选的，处理器110可集成应用处理器和调制解调处理器，可选地，应用处理器主要处理操作系统、用户界面和应用程序等，调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器110中。
移动终端100还可以包括给各个部件供电的电源111(比如电池)，优选的，电源111可以通过电源管理系统与处理器110逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。
尽管图1未示出，移动终端100还可以包括蓝牙模块等，在此不再赘述。
为了便于理解本申请实施例，下面对本申请的移动终端所基于的通信网络系统进行描述。
请参阅图2，图2为本申请实施例提供的一种通信网络系统架构图，该通信网络系统为通用移动通信技术的LTE系统，该LTE系统包括依次通讯连接的UE(User Equipment，用户设备)201，E-UTRAN(Evolved UMTS Terrestrial Radio Access Network，演进式UMTS陆地无线接入网)202，EPC(Evolved Packet Core，演进式分组核心网)203和运营商的IP业务204。
可选地，UE201可以是上述移动终端100，此处不再赘述。
E-UTRAN202包括eNodeB2021和其它eNodeB2022等。可选地，eNodeB2021可以通过回程(backhaul)(例如X2接口)与其它eNodeB2022连接，eNodeB2021连接到EPC203，eNodeB2021可以提供UE201到EPC203的接入。
EPC203可以包括MME(Mobility Management Entity，移动性管理实体)2031，HSS(Home Subscriber Server，归属用户服务器)2032，其它MME2033，SGW(Serving Gate Way，服务网关)2034，PGW(PDN Gate Way，分组数据网络网关)2035和PCRF(Policy and Charging Rules Function，政策和资费功能实体)2036等。可选地，MME2031是处理UE201和EPC203之间信令的控制节点，提供承载和连接管理。HSS2032用于提供一些寄存器来管理诸如归属位置寄存器(图中未示)之类的功能，并且保存有一些有关服务特征、数据速率等用户专用的信息。所有用户数据都可以通过SGW2034进行发送，PGW2035可以提供UE201的IP地址分配以及其它功能，PCRF2036是业务数据流和IP承载资源的策略与计费控制策略决策点，它为策略与计费执行功能单元(图中未示)选择及提供可用的策略和计费控制决策。
IP业务204可以包括因特网、内联网、IMS(IP Multimedia Subsystem，IP多媒体子系统)或其它IP业务等。
虽然上述以LTE系统为例进行了介绍，但本领域技术人员应当知晓，本申请不仅仅适用于LTE系统，也可以适用于其他无线通信系统，例如GSM、CDMA2000、WCDMA、TD-SCDMA以及未来新的网络系统(如5G)等，此处不做限定。
基于上述移动终端硬件结构以及通信网络系统，提出本申请各个实施例。
为便于理解，下面先对本申请实施例可能涉及到的专业术语进行解释。
一、预测模式
在对视频中的图像进行编码的过程中，对图像块进行预测是一个必不可少的步骤，通过对图像块进行预测得到预测块，构造能量较小的残差块，可以减少传输比特。对图像块的预测可以通过一些预设的预测模式实现，预测模式可以包括帧间预测模式和帧内预测模式。
(1)帧间预测模式：帧间预测模式利用不同图像的像素之间的相关性来去除时间冗余，相比于帧内预测模式，是一种编码效率更高的预测模式。
(2)帧内预测模式：帧内预测模式利用视频空间域的相关性，使用同一帧中邻近的已编码像素预测当前像素。例如当前编码单元(Coding Unit，CU)可以利用相邻位置的重建像素来预测当前CU中的像素。帧内预测模式可以为：直流(Direct Current,DC)模式，或平面(PLANAR)模式，或角度模式。
二、划分模式
为了更好地编码图像中运动物体的边界部分，可以对图像划分为不同区域进行预测，在下一代视频压缩标准(Versatile Video Coding，VVC)中还提出了几何划分模式(Geometric partitioning mode,GPM)，GPM模式可以对图像中运动物体的边界进行更为精细的划分，通过分割线和运动物体的边界进行贴合，将运动物体的边缘编码单元(Coding Unit)划分为矩形或者非矩形的子编码单元进行预测，从而得到整个编码单元的预测值。
第一实施例
请参阅图3，图3是根据第一实施例示出的一种图像处理方法的流程示意图，该实施例中的执行主体可以是一个计算机设备或者是多个计算机设备构成的集群，该计算机设备可以是智能终端(如前述移动终端100)，也可以是服务器，此处，以本实施例中的执行主体为智能终端为例进行说明。
S301，根据预设预测模式确定目标图像块中对应分区的预测结果集合。
目标图像块是指输入视频图像(即视频帧)中当前正在编码的图像块，可简称为当前块或者当前图像块或者当前编码块。目标图像块可以是输入视频图像中的一个编码树单元(Coding Tree Units，CTU)，或者是一个编码单元(Coding Unit，CU)，或者是变换单元(Transform Unit，TU)等等。对此不做限制。目标图像块可以是方形块(即图像块的尺寸为正方形)，也可以非方形块。非方形块可以是矩形尺寸的图像块，包括水平块(宽度大于高度)和/或垂直块(高度大于宽度)，举例来说，当目标图像块为CU时，CU可以是方形块或者是非方形块。在此不做限制。
可选地，所述目标图像块包括第一分区和/或第二分区，所述第一分区和/或第二分区是由分割线划分得到的图像区域。目标图像块中对应分区可以是第一分区或者是第二分区。第一分区和/或第二分区是目标图像块中的矩形或者是非矩形区域，且第一分区和第二分区是相对而言的。在一实施例中，第一分区和第二分区为通过GPM模式得到的关于目标图像块的矩形、三角形、或梯形区域。举例来说，如图4a中的(1)所示，使用水平方向的分割线划分目标图像块，在水平方向的分割线上方的图像区域可以称为第一分区，在水平方向的分割线下方的图像区域可以称为第二分区。反之，也可以将在水平方向的分割线下方的图像区域称为第一分区，在水平方向的分割线上方的图像区域称为第二分区，且两个分区都是矩形区域。如图4a中的(2)所示，分割线划分的目标图像块得到的两个分区中，一个为三角形区域，另一个为非矩形区域。目标图像块中对应分区是指目标图像块中包含的一个分区，该分区是目标图像块中的一个图像区域。目标图像块中对应分区可以是第一分区或者第二分区。
可选地，所述预设预测模式包括所述目标图像块中分割线划分的分区所使用的预测模式。目标图像块中分割线划分的分区包括第一分区和/或第二分区，对应地，预设预测模式包括目标图像块中第一分区所使用的预测模式和/或第二分区所使用的预测模式。
在一实施例中，可将相邻图像块所使用的预测模式作为目标图像块中分割线划分的分区所使用的预测模式。可选地，可将至少一个相邻图像块所使用的预测模式中使用次数最多或次数较多的预测模式作为目标图像中分割线划分的分区所使用的预测模式。在另一实施方式中，可以通过其他方式得到目标图像中分割线划分的分区所使用的预测模式。相邻图像块是与目标图像块相邻的已编码图像块，相邻图像块可以包括一个或者多个。当相邻图像块包括一个时，可以直接将相邻图像块所使用的预测模式确定为预设预测模式，当相邻图像块包括多个时，可以通过统计各种预测模式的使用次数，并将使用次数最多的预测模式确定为预设预测模式。在上述实施方式中，无需通过计算率失真代价来确定采用的预设预测模式。然而，本发明并非限于此，在上述实施方式中，也可以通过计算率失真代价来确定预设预测模式。由于相邻图像块与目标图像块之间的相关性，通过参考相邻图像块所使用的预测模式，当前编码块使用的预设预测模式既可以保证编码质量，还可以提高编码效率。在其他实施例中，针对目标图像块中分割线划分的分区所使用的预测模式，可以采用率失真代价确定。在此方式下，预设预测模式是与目标图像块中对应分区更加匹配的预测模式，这样可以有效保证预测结果的准确性，相比于前两种方式，准确度会有所提升。对于不同分区来说，不同分区对应的预设预测模式可能不同。例如目标图像块中的第一分区和第二分区所使用的预测模式不同。
在一种实现方式中，当预设预测模式包括目标图像块对应分区使用的预测模式时，对于预设预测模式的确定过程包括下述1)和2)：
1)根据第一划分模式包括的每种划分模式应用于所述目标图像块的率失真代价，确定所述目标图像块的目标划分模式。
针对此步骤，首先可以确定第一划分模式包括的每种划分模式应用于所述目标图像块的率失真代价，之后将所述率失真代价最小的划分模式确定为所述目标图像块中分割线划分的分区所使用的目标划分模式。也就是说，可以遍历第一划分模式包括的所有划分模式，确定出目标图像块分别采用各种划分模式所需要的率失真代价，为了实现最优的编码性能，寻求在一定码率下尽可能地减少视频失真或者是在失真允许的范围内将视频压缩到最小的目标，可以将各个划分模式对应的率失真代价进行比较，将率失真代价最小的划分模式确定出来并将该率失真代价最小的划分模式作为目标图像块的目标划分模式，该目标划分模式下用于划分目标图像块的不同分区，进而实现目标图像块的最优编码。
2)根据第一划分模式参数集合确定所述目标划分模式对应的目标划分模式参数。可选地，有关目标划分模式对应的模式参数可以从第一划分模式参数集合中查询出来，并将该模式参数作为目标图像块所要使用的目标划分模式参数，后续可以按照该目标划分模式参数划分目标图像块并进行预测，得到目标图像块的预测结果。
为便于理解，下面以第一划分模式为GPM模式，划分模式为GPM模式对应的64种划分模式中的任一种，第一划分模式参数集合为GPM映射表，目标划分模式参数为GPM参数为例，对上述过程进行说明：首先，编码器确定当前块的颜色分量(包括，亮度分量和/或色度分量)；基于当前块的参数，利用多种预测模式(包括帧内预测模式和/或帧间预测模式)分别对颜色分量进行预测编码，计算每一种预测模式对应的率失真代价，以从多种预测模式分别对应的率失真代价中确定最小率失真代价。最后，将最小率失真代价对应的预测模式确定为当前块的预测模式参数。当最小率失真代价对应的预测模式为GPM模式时，则将GPM模式确定为当前块的预测模式参数。并将GPM模式对应的预测模式参数二值化后打包至比特流中进行传输。
通过遍历GPM模式对应的64种划分模式，可以确定出率失真代价最小的划分模式，并将率失真代价最小的划分模式作为当前块的目标划分模式。可选地，依据目标划分模式，通过GPM分割索引gpm_partition_idx、角度索引angleIdx和距 离索引distanceIdx的映射表，确定出目标划分模式对应的目标GPM分割索引merge_gpm_partition_idxT、目标角度索引angleIdxT和目标距离索引distanceIdxT。关于GPM分割索引、角度索引以及距离索引的参数映射表的确定。对于GPM映射表如下表1所示。
表1GPM映射表
如表1所示，GPM模式包括64种划分模式，每种划分模式与一条分割线对应。GPM参数包括的分割索引gpm_partition_idx、角度索引angleIdx和距离索引distanceIdx。需要说明的是，角度索引angleIdx取不同值时，对应于图4b所示的不同角度可选地，i为1～24。距离索引distanceIdx取不同值时，对应于图4c中的ρj，可选地，j为0～3。由于角度索引angleIdx，距离索引distanceIdx的不同取值的组合分别构成不同的划分方式。例如，如图4d所示。
在一个实施例中，可以利用一查找表来表示分割索引pm_partition_idx与角度索引angleIdx，距离索引distanceIdx之间的关系。例如，如下表2所示。
表2查找表
可选地，角度索引angleIdx对应于角度的正弦值，距离索引distanceIdx对应于ρj。在一种实施方式中，可以设置关于angleIdx与的映射表，如下表3所示。在另一实施方式中，还可以设置关于angleIdx的中间变量与的映射表。
表3角度映射表
在另一实施例中，也可以设置关于角度索引angleIdx与斜率的映射表。例如，使用多种固定斜率{slope0,slope1,slope2,...,slopen}去构造不等间隔的角度表。
由于预测模式参数可以打包至比特流中进行传输，因此在解码端通过解析比特流可以确定目标图像块的预测模式参数，该预测模式参数是用于指示目标图像块所使用的预测模式的参数信息。若预测模式参数为用于指示使用第一划分模式的目标划分模式参数，那么可以由目标划分模式参数确定具体使用何种类型的第一划分模式。可选地，目标划分模式参数包括分割索引、角度索引以及距离索引中至少一项，此外，所述目标划分模式参数包括用于指示所述目标图像块中对应分区所使用的预测模式的预测模式指示信息。该预测模式指示信息用于在解码端指示目标图像块中对应分区所使用的预测模式。换而言之，在解码端，可以：根据所述目标划分模式参数中包括的预测模式指示信息，确定所述目标图像块包括的分区所使用的预测模式。
可选地，预测模式指示信息包括用于指示所述第一分区所使用的预测模式的预测模式指示信息，和/或，用于指示所述第二分区所使用的预测模式的预测模式指示信息。这些预测模式指示信息可以是目标图像块对应分区在编码端使用的预测模式类型的标记或者索引。
在一种实现方式中，预测模式包括帧内预测模式和/或帧间预测模式。若预设预测模式为帧内预测模式，则可以利用目标图像块中对应分区的已编码的像素预测当前图像块中的像素(例如角度预测模式)，也可以利用已经编码的亮度分量预测当前图像块的颜色分量，例如CCLM(交叉分量线性模型)模式中用Y分量的像素预测色度分量的像素。若预设预测模式为帧内预测模式，则可以利用目标图像块所在图像的参考图像确定运动矢量，并基于运动矢量进行预测得到预测结果。
可选地，用于指示编码端所使用的运动矢量的运动矢量索引被编码在比特流中。运动矢量包括第一运动矢量和/或第二运动矢量，运动矢量索引例如是gpm_idx0[x0][y0]和gpm_idx1[x0][y0]。需要说明的是，gpm_idx0[x0][y0]和gpm_idx1[x0][y0]也可被发信于合并数据merge_data()中。gpm_idx0[x0][y0]表示第一运动矢量在合并候选列表中的位置，gpm_idx1[x0][y0]表示第二运动矢量在合并候选列表中的位置。由此，当预测模式参数为目标划分模式参数时，目标划分模式参数还包括运动矢量索引。
可选地，运动矢量索引包括第一运动矢量索引和/或第二运动矢量索引。运动矢量对应包括第一运动矢量和/或第二运动矢量，该运动矢量可以是目标图像块对应分区使用帧间预测模式所需要的。示例性地，当预测模式指示信息指示第一分区采用帧间预测模式，第二分区采用帧内预测模式，则第一运动矢量和/或第二运动矢量是第一分区的运动矢量；当预测模式指示信息指示第二分区采用帧间预测模式，第一分区采用帧内预测模式，则第一运动矢量和/或第二运动矢量是第二分区的运动矢量。
对于目标图像块中对应分区的预测结果集合的确定方式可以采用目标图像块对应分区的预测模式确定目标图像块对应分区的预测结果集合。
可选地，所述预测结果集合包括第一分区的第一预测结果集合和/或所述第二分区的第二预测结果集合。展开来说，可以：根据目标图像块中第一分区所使用的预测模式确定第一预测结果集合，和/或根据目标图像块中第二分区所使用的预测模式确定第二预测结果集合。当目标图像块包括第一分区和第二分区时，目标图像块的预测结果集合包括第一预测结果集合和第二预测结果集合。需要说明的是，第一分区的第一预测结果集合(或者第二分区的第二预测结果集合)是指以第一分区(或者第二分区)中的数据按照预设预测模式进行预测得到的，在后续确定目标图像块的预测结果使用中，第一预测结果集合和第二预测结果集合可以被第一分区和第二分区共用。
在一个实施例中，目标图像块对应分区的预测结果集合用于确定目标图像块的预测结果。可选地，根据第一分区的第一预测结果集合和/或第二分区的第二预测结果集合可以确定目标图像块的预测结果。对于预测结果的具体确定方式，可以参见下述实施例的介绍，在此先不做详述。
综上所述，本申请实施例提供的图像处理方案通过预设预测模式对目标图像块进行预测，确定出目标图像块对应分区对预测结果集合，进而可以得到预测结果。在预设预测模式的选择上，可以参考与目标图像块相邻的图像块或者是采用率失真代价计算，满足条件的预测模式均可以作为预设预测模式，预设预测模式的选择范围较大，与目标图像块对应分区配合使用的灵活性高；并且，根据预设预测模式确定的目标图像块中对应分区的预测结果集合，综合不同分区对弈的预测结果集合可以保证目标图像块的预测结果的准确度。由此，预设预测模式的使用灵活性与预测结果的准确度可以达到较好的平衡。
第二实施例
请参阅图5，图5是根据第二实施例示出的一种图像处理方法的流程示意图，该实施例中的执行主体可以是一个计算机设备或者是多个计算机设备构成的集群，该计算机设备可以是智能终端(如前述移动终端100)，也可以是服务器，此处，以本实施例中的执行主体为智能终端为例进行说明。
在预设预测模式为目标图像块中分割线划分的分区所使用的预测模式时，可以按照S501和S502中介绍的内容确定目标图像块中对应分区的预测结果集合。可选地，预测模式包含第一预测模式和/或第二预测模式。由于对应分区使用的预测模式可以为第一预测模式或者是第二预测模式，因此，预设预测模式可以为第一预测模式或者第二预测模式。
S501，若所述预测模式为第一预测模式，则根据所述目标图像块中对应分区的运动矢量确定所述目标图像块中对应分区的预测结果集合。
可选的，第一预测模式为帧间预测模式。此时目标图像块分割线的分区使用的预测模式为帧间预测模式，目标图像块中分割线划分的分区包括第一分区和/或第二分区。目标图像块对应分区为使用帧间预测模式的分区，该分区可以包括第一分区和/或第二分区，换而言之，目标图像块中第一分区使用帧间预测模式，或者，目标图像块中第二分区使用帧间预测模式，或者，目标图像块中的第一分区和第二分区均使用帧间预测模式。
在帧间预测模式下，可以基于运动矢量来确定目标图像块中对应分区的预测结果集合。可选地，当目标图像块中的第一分区使用帧间预测模式时，根据第一分区的运动矢量确定第一预测结果集合，和/或，当目标图像块中的第二分区使用帧间预测模式时，根据第二分区的运动矢量确定第二预测结果集合。在一种实施方式中，可以具有如下步骤：根据所述目标图像块的合并候选列表和/或目标参考采样点确定所述目标图像块中对应分区的第一运动矢量和/或第二运动矢量；根据所述第一运动矢量和/或所述第二运动矢量确定所述目标图像块中对应分区的预测结果集合。
在确定运动矢量之前，可以具有如下步骤：构建目标图像块的合并候选列表。可选地，合并候选列表基于空域合并候选列表得到。合并候选列表是一种帧间预测候选列表，可以用于在单向预测候选或者双向预测候选时确定第一运动矢量和/或第二运动矢量。
下面对目标图像块的合并候选列表的构建过程进行介绍。
请参阅图6a，图6a为本申请实施例提供的一种示例性地空域合并候选列表的相邻块位置示意图。空域合并候选列表最多可选出4个候选运动矢量，构造顺序为：上侧相邻块B1、左侧相邻块A1、右上侧相邻块B0、左下侧相邻块A0、左上侧相邻块B2，参考帧对应位置块col为相邻块的运动矢量信息依次排列。需要说明的是，只有其他位置不可用时才考虑B2。在添加左下侧相邻块A0之后，需要进行冗余检测，以确保列表中不具有相同运动信息的候选。此外，合并候选列表中还可以添加历史参考块his，第一候选运动矢量、第二候选运动矢量的平均运动矢量avg，以及零运动矢量0。请参阅图6b，图6b为本申请实施例提供的一种示例性地合并候选列表的示意图。该合并候选列表中包括图6a中所示的5个相邻块的运动信息，序号分别为0、1、2、3、4，每个相邻块包含双向预测运动矢量信息，即列表0(list0)和列表1(list1)分别对应的运动矢量信息。
在一个实施例中，帧间预测模式包括单向预测模式和双向预测模式。对于目标图像块中采用帧间预测模式的分区来说，可以使用单向预测模式或者双向预测模式，单向预测模式和/或双向预测模式可以通过目标图像块中的一个分区使用的单向预测或者双向预测的指示信息(例如标记或者索引)来确定。由此，可以根据该指示信息确定目标图像块中的一个分区具体采用单向预测模式还是双向预测模式，之后，进一步从合并候选列表中确定该分区的单向运动矢量(第一运动矢量或者第二运动矢量)或者双向运动矢量(第一运动矢量和第二运动矢量)：当目标图像块中的第一分区(或者第二分区)使用单向预测指示信息时，确定目标图像块中的第一分区(或者第二分区)使用单向预测模式，并从合并候选列表中确定第一分区(或者第二分区)的第一运动矢量或者第二运动矢量；当目标图像块中的第一分区(或者第二分区)使用双向预测指示信息时，确定目标图像块中的第一分区(或者第二分区)使用双向预测模式，并从合并候选列表中确定第一分区(或者第二分区)的第一运动矢量和第二运动矢量。
第一运动矢量和/或第二运动矢量均是帧间预测运动矢量，在通过合并候选列表得到目标图像块中对应分区的第一运动矢量和/或第二运动矢量之后，可以根据第一运动矢量和/或第二运动矢量进行运动补偿，确定目标图像块关于该帧间预测运动矢量的帧间预测值，从而得到预测结果集合，该预测结果集合中包含帧间预测值。
当目标图像块包括第一分区和第二分区，且第一分区和第二分区均使用帧间预测模式时，对于每个分区均可以按照上述方式确定目标图像块的预测结果集合，且此时预测结果集合包括第一预测结果集合和第二预测结果集合，即：根据目标图像块中第一分区的运动矢量确定目标图像块的第一预测结果集合，以及根据目标图像块中第二分区的运动矢量确定目标图像块的第二预测结果集合。此时第一预测结果集合和第二预测结果集合均是通过帧间预测得到的。
需要说明的是，对于使用帧间预测的分区来说，除了通过空域合并候选列表得到该分区对应的帧间预测运动矢量，也可以利用分区的目标参考采样点来确定该分区对应的帧间预测模式下的运动矢量。在一实施方式中，若B分区使用帧间预测模式，并且对于B分区可用的参考采样点(例如，图6c中的第一参考采样点)中存在一个参考采样点x使用帧间预测，则可以利用该参考采样点x的运动矢量确定B分区对应的帧间预测模式下的运动矢量。可选地，可以直接将参考采样点x的运动矢量作为B分区的运动矢量。可选地，参考采样点x为位于可用的参考采样点中的中心或中间的参考采样点。
S502，若所述预测模式为第二预测模式，则根据所述目标图像块中对应分区的目标参考采样点确定所述目标图像块中对应分区的预测结果集合。
可选地，若所述预测模式为第二预测模式，根据所述目标图像块中对应分区的目标参考采样点的取值确定所述目标图像块中对应分区的预测结果集合。例如，若目标参考采样点为亮度采样点，则根据该亮度采样点的像素值确定所述目标图像块中对应分区的预测结果集合。在一实施例中，可以将该亮度采样点的像素值作为对于分区中的采样点的像素值。
可选地，第二预测模式为帧内预测模式。在解码端，目标图像块对应分区使用的预测模式为第一预测模式或者第二预测模式基于预测指示信息确定。
可选地，第二预测模式包括至少一种类型的第二预测模式。第二预测模式为帧内预测模式，则包括多种类型的帧内预测模式。举例来说，在VVC中，为了捕捉自然视频中呈现的任意边缘方向，帧内预测模式所包括的预测模式类型增加了更多的预测方向，以提升帧内预测的精度。相比于HEVC中的帧内预测模式，VVC包括的角度预测模式扩展到了65个，同时保留Planar模式和DC模式，共计67种类型的帧内预测模式。详细地如下表4所示。
表4帧内预测模式
至少一种类型的帧内预测模式包括Planar模式、DC模式以及角度预测模式。Planar模式用于解决渐变式的平滑纹理区域的，DC模式适用于大面积平坦区域，角度预测模式包括角度预测模式2至角度预测模式66，不同的角度预测模式的预测方向不同，以更好地适应视频内容中不同方向的纹理。
在一种实现方式中，当预测模式为第二预测模式时，还可以：从所述至少一种类型的第二预测模式中确定所述目标图像块对应分区所使用的目标类型的第二预测模式。
目标图像块的分区包括第一分区和/或第二分区。第二预测模式可以由预测指示信息指示，当第一分区的预测指示信息指示第一分区所使用的预测模式为帧内预测模式时，可以具体确定目标类型的帧内预测模式。可选地，第二预测模式为帧内预测模式时，目标类型的第二预测模式可以为Planar模式(即平面模式)、DC模式以及65种角度预测模式中的任一种。
若目标图像块对应分区的预测模式为帧内预测模式，则基于帧内最可能模式索引确定目标图像块对应分区使用的目标类型的帧内预测模式。帧内最可能模式索引是指最可能使用的目标类型的帧内预测模式的索引，对于帧内最可能模式索引可以通过构建最可能模式(Most Probable Mode，MPM)列表并从中确定。在VVC内MPM list里有6个预测模式，如果当前块的预测模式在MPM中只需要编码其索引，该索引即帧内最可能模式索引。通过最可能模式列表的构建可以有效减少数据量，提高编码效率。
可选地，所述目标类型的第二预测模式用于确定所述目标图像块的预测结果集合。在目标类型的第二预测模式下，可以采用目标类型的第二预测模式下设定的处理方式利用目标参考采样点确定目标图像块的预测结果集合，进而确定目标图像块的预测结果。例如在目标类型的第二预测模式为Planar模式，则可以使用水平方向和垂直方向的线性滤波器，取水平方向和垂直方向上分别相邻的像素点(即目标参考采样点)的平均值，进而得到预测结果集合。
在帧内预测模式下，可以依据邻近的已编码像素预测当前像素，由此，目标参考采样点是指与目标图像块中至少一个分区邻近的已编码像素点，参考采样点的采样值即为像素值。
可选地，所述目标参考采样点包括第一参考采样点和/或第二参考采样点，所述第一参考采样点和/或所述第二参考采样点不同。针对目标图像块中的不同分区，第一参考采样点和/或第二参考采样点的选取是不同的。可选地，第一参考采样点和第二参考采样点有如下不同：
①所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻。
当目标图像块中对应分区为第一分区时，第一参考采样点与第一分区相邻且与第二分区不相邻，第二参考采样点与第二分区相邻且与第一分区不相邻；当目标图像块中对应分区为第二分区时，第一参考采样点与第二分区相邻且与第一分区不相邻，第二参考采样点与第一分区相邻且与第二分区不相邻。请参阅图6c所示的一种示例性地目标参考采样点的示意图。如图6c中的(1)所示，对于不同的分区来说，第一参考采样点(或者第二参考采样点)是不同的。第一参考采样点可以是与对应分区相邻的参考采样点，第二参考采样点可以是与目标图像块中与对应分区不相邻、且与除对应分区之外的另一个分区相邻的参考采样点。例如，如图6c中的(1)所示，对于第一分区来说，第一参考采样点可以是与第一分区相邻的参考采样点，第二参考采样点可以是与目标图像块中与第一分区不相邻、且与第二分区相邻的参考采样点。对于第二分区来说，第一参考采样点可以是与第二分区相邻的参考采样点，第二参考采样点可以是与目标图像块中与第二分区不相邻、且与第一分区相邻的参考采样点。
可选地，第一参考采样点或第二参考采样点除了包括与对应分区相邻的参考采样点，还包括与对应分区不相邻的参考采样点。如图6c中的(2)所示，当目标图像块中对应分区为第一分区时，第一参考采样点包括与第一分区相邻且与第二分区不相邻的参考采样点，第二参考采样点包括与第二分区相邻且与第一分区不相邻的参考采样点，以及与第一分区和第二分区均不相邻的参考采样点；当目标图像块中对应分区为第二分区时，第一参考采样点包括与第二分区相邻且与第一分区不相邻的参考采样点，以及与第一分区和第二分区均不相邻的参考采样点，第二参考采样点包括与第一分区相邻且与第二分区不相邻的参考采样点。
②所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同。
目标参考采样点相对于对应分区的位置关系包括目标参考采样点与对应分区之间的相邻关系和/或距离。当目标图像块中对应分区为第一分区时，第一参考采样点相对于第一分区的位置关系和第二参考采样点相对于第一分区的位置关系不同，可选地，位置关系可以是如①所述的内容为与分区相邻与否的关系。此外，根据目标参考采样点与对应分区的距离远近也可以表示位置关系：当目标图像块中对应分区为第一分区时，第一参考采样点相对于第一分区的最短距离小于第二参考采样点相对于第二分区的最短距离。
③所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
此处目标图像块为编码单元，该编码单元可以是编码树单元中的任一个正在编码的编码单元。在VVC编码中，可将一帧图像划分为多个编码树单元依次进行编码，每个编码树单元又划分为多个编码单元依次进行编码。当一个编码树单元中的编码单元编码结束时，接着对下一个编码树单元中的编码单元进行编码，如图6d所示。当目标图像块为编码树单元中的第一个编码的编码单元时，目标参考采样点包括与编码树单元相邻的第一参考采样点和第二参考采样点；当目标图像块为编码树单元中非首个编码的编码单元时，目标参考采样点包括与编码树单元相邻的参考采样点和/或与编码单元相邻的参考采样点，如图6c中的(1)所示，目标参考采样点包括的第一参考采样点和第二参考采样点均与编码单元相邻。这些参考采样点都是已经编码的像素点，鉴于相邻的两个像素的亮度和色度值之间经常是比较接近的，也就是颜色是逐渐变化的，利用这种相关性进行压缩可以达到有效去除视频时域冗余的目的。对于目标参考采样点的具体确定方式可以参见下述实施例，在此先不做详述。
在又一个实施例中，若目标图像块中第一分区可以使用第一预测模式，第二分区可以使用第二预测模式，则可以同时执行如S501和S502描述的内容，且S501中目标图像块的对应分区为第一分区，S502中目标图像块的对应分区为第二分区。同理，对于第二分区可以使用第一预测模式，第一分区可以使用第二预测模式，也可以同时执行如S501和S502描述的内容，且S501中目标图像块的对应分区为第二分区，S502中目标图像块的对应分区为第一分区。可选地，第一预测模式为帧间预测模式，可以包括双向预测模式或者单向预测模式。可选地，采用帧内预测的分区可以进行单向、或双向的运动补偿，来得到预测值。第二预测模式为帧内预测模式，帧内预测模式包括多种角度预测模式、平面模式以及DC模式，则目标图像块在使用划分模式的情况下，划分模式划分的分区所使用的预测模式可以有以下组合：双向预测模式与角度预测模式、双向预测模式与平面模式、双向预测模式与DC模式等等，以上所列举的仅为参考示例，为了避免冗余，这里不再一一列举，实际开发或运用中，可以根据实际需要灵活组合，但任一组合均属于本申请的技术方案，也就覆盖在本申请的保护范围之内。
在一个实施例中，目标图像块对应分区的预测结果集合用于确定目标图像块的预测结果。可选地，包括以下步骤1)和2)：
1)基于所述目标图像块的目标划分模式参数，确定分区权重。
可选地，基于所述目标划分模式参数包括的角度索引和距离索引确定分区权重。分区权重是指图像块包括的两个分区所对应的权重，分区权重用于和对应分区中的各个像素点进行加权，得到加权预测像素值，记为predSamples，对应分区的加权预测像素值可以作为预测结果。
根据角度索引angleIdx和距离索引distanceIdx可以得到分割线的直线方程，可选地，根据角度索引angleIdx可以确定直线方程中的和根据距离索引distanceIdx可以确定直线方程中的ρ。对于直线方程的表达式如下：
可选地，(xc，yc)为目标图像块中任一采样点的坐标。
如图6e所示，图6e是本申请实施例提供的一种像素点和分割线的距离分析的示意图。若像素点(xc，yc)为当前块中的像素，则基于上述直线方程，可以得到像素点(xc，yc)到分割线的距离为：
当ρ为0时，分割线如前述图4b所示。
为此，目标划分模式参数中包括的角度索引和距离索引可以确定出目标图像块中各个像素点与分割线之间的距离，可以根据该距离确定出对应于目标图像块的各像素值的权重。
可选地，根据像素点(xc，yc)相对于分割线距离的远近设置不同的权重，例如，若像素点(xc，yc)到分割线的距离大于或等于设定距离阈值，则将像素点(xc，yc)对应的权重设置为K1，反之，则将像素点(xc，yc)对应的权重设置为K2。显而易见地是：小于该设定距离阈值的像素点均位于分割线附近，大于设定距离阈值的像素点则远离分割线。对于目标图像块中第一分区和第二分区对应的像素点都采用上述规则进行权重设置，得到分区权重。此外，对于分割线两侧的像素点，即不同分区中包括的像素点，可以设置互不相同的固定权重，可见，各个像素值的权重不仅与距离分割线的远近相关，还与各个像素所在分区相关。
通过这样的方式设置分区权重，可以对目标图像块中以分割线为界限的两个图像区域中的像素点予以不同的关注度，并且，靠近分割线和不靠近分割线的像素点所对应的权重也可以是不同的，分割线两侧的分区中越靠近分割线的像素点对应的权重越大，这样可以更好地沿分割线边缘融合目标图像块的两个分区，得到预测结果。
2)基于所述分区权重、第一预测结果集合以及第二预测结果集合中的至少一项，确定所述目标图像块的预测结果。
在一个实施例中，第一预测结果集合是采用第一预测模式(例如帧间预测模式)得到的，或者是采用第一预测模式(例如帧内预测模式)得到的，第二预测结果集合同理。
可选地，此步骤的可选实现方式可以是：基于所述第一权重、所述第二权重、所述目标图像块包括的采样点在第一预测结果集合中的第一预测结果以及所述采样点在第二预测结果集合中的第二预测结果中的至少一项，确定所述采样点的预测结果。
对于第一分区来说，可以：基于第一权重、所述目标图像块包括的采样点在第一预测结果集合中的第一预测结果以及所述采样点在第二预测结果集合中的第二预测结果中的至少一项，确定所述目标图像块包括的第一分区中的采样点的预测结果。
第一权重可以是第一分区对应的权重集合，可以称为第一权重集合，包括{w11,w12}，基于采样点与分割线的距离确定权重的规则确定分别表示第一分区中靠近分割线对应的权重和远离分割线对应的权重，靠近或者远离的区域通过距离阈值来划分。利用第一权重对目标图像块包括的第一分区中的采样点在第一预测结果集合中对应的第一预测结果，及在第二预测结果集合中对应的第二预测结果进行加权求和处理，得到融合后的预测值，即第一分区中采样点的预测结果。具体表达式如下：
P(xc1，yc1)＝f(w11*P11+w12*P12)
可选地，(xc1，yc1)表示第一分区中的采样点，P11表示第一分区的采样点在第一预测结果集合中的第一预测结果，P12表示第一分区的采样点在第二预测结果集合中的第二预测结果，w11和w11之和为1，依据上述权重的设置规则，w11可以设置为K1，w12对应为(1-K1)；或者w11设置为K2，w12对应为(1-K2)。
对于第二分区来说，可以：基于第二权重、所述目标图像块包括的采样点在第一预测结果集合中的第一预测结果以及所述采样点在第二预测结果集合中的第二预测结果中的至少一项，确定所述目标图像块包括的第二分区中的采样点的预测结果。
同理，第二权重是第二分区对应的权重集合，可以称为第二权重集合，包括{w21,w22}，分别表示第二分区中靠近分割线对应的权重和远离分割线对应的权重，靠近或者远离的区域通过距离阈值来划分，该距离阈值可以和第一分区使用距离阈值相同，也可以不同。可选地，目标图像块包括的第二分区中的采样点在第一预测结果集合以及在第二预测结果集合中都对应有相应的预测结果，因此，可以利用第二权重对目标图像块包括第二分区中的采样点在第一预测结果集合中对应的第一预测结果，及在第二预测结果集合中对应的第二预测结果进行加权求和处理，得到融合后的预测值，即第二分区中采样点的预测结果。具体表达式如下：
P(xc2，yc2)＝f(w21*P21+w22*P22)
可选地，(xc2，yc2)表示第二分区中的采样点，P21表示第一预测结果，P22表示第二预测结果，依据上述权重的设置规则，w21可以设置为K1，w22对应为(1-K1)；或者w21设置为K2，w22对应为(1-K2)；或者w21可设置为不同于K1或者K2的权重，但w21和w21之和为1。
需要说明的是，对于第一分区和第二分区中的各个采样点都可以按照上述方式确定出对应的预测结果，进而得到第一分区的预测结果和第二分区的预测结果，第一分区的预测结果包括第一分区中所有采样点的预测结果，第二分区的预测结果包括第二分区中所有采样点的预测结果。可以将第一分区的预测结果和第二分区的预测结果组合，可以得到所有分区的采样点的预测结果，该预测结果就是目标图像块的预测结果。或者将两个分区的预测结果进行边缘融合得到目标图像块的预测结果。
综上所述，本申请实施例提供的图像处理方案，可以在不同的预测模式下，根据目标图像块对应分区的不同信息确定预测结果集合，即在预测模式为第一预测模式时，可以根据目标图像块中使用第一预测模式的分区的运动矢量确定第一预测结果集合，在预测模式为第二预测模式时，可以根据目标图像块中使用第二预测模式的分区的目标参考采样点确定第二预测结果集合，并且第一预测模式和第二预测模式均包括多种类型，尤其是第二预测模式的可选择范围更广，与目标图像块使用的划分模式的组合也更多，并且对于预测结果的准确度有所提升。
第三实施例
请参阅图7，图7是根据第三实施例示出的一种图像处理方法的流程示意图，该实施例中的执行主体可以是一个计算机设备或者是多个计算机设备构成的集群，该计算机设备可以是智能终端(如前述移动终端100)，也可以是服务器，此处，以本实施例中的执行主体为智能终端为例进行说明。
在预设预测模式为目标图像块中对应分区所使用的预测模式，且所述分区所使用的预测模式为第二预测模式时，根据目标图像块中对应分区的目标参考采样点确定目标图像块中对应分区的预测结果集合的可选实施方式，可以参见如下述S701和S702介绍的内容。
S701，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点。
可选地，目标图像块包括第一分区和/或第二分区，目标图像块中对应分区为第一分区或者第二分区。当目标图像块包括第一分区和第二分区时，可以确定第一分区的第一参考采样点和/或第二参考采样点，以及第二分区的第一参考采样点和/或第二参考采样点，由于针对不同分区确定的参考采样点的逻辑是类似的，只是第一参考采样点和/或第二参考采样点对于不同分区来说的内容不同。因此，在本实施例中，以确定一个分区(例如第一分区)的第一参考采样点和/或第二参考采样点为例进行说明。
在一个实施例中，S701的实现方式可以是：根据所述目标图像块中对应分区与所述目标图像块使用的分割线在所述目标图像块中的边界采样点之间的位置关系，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点。
此处的目标图像块对应分区是指目标图像块中使用第二预测模式的分区，可选地，第二预测模式为帧内预测模式。目标图像块中对应分区与边界采样点之间的位置关系是指采用第二预测模式的分区中的采样点和边界采样点之间的相对位置关系，根据该位置关系可以确定第一分区或者第二分区对应的参考采样点。目标图像块使用的分割线经过目标图像块的不同边界以将目标图像块划分为不同的图像区域，在此称为第一分区和第二分区。目标图像块中包括至少一个采样点，可选地，采样点为像素点。由于分割线与目标图像块的边界之间相交，因此分割线在目标图像块中的边界采样点是指目标图像块中与已编码像素点相邻的N个采样点中分割线所经过的采样点，N为正整数。示例性地，如图8a所示，为分割线所在的边界采样点的示意图。分割线将目标图像块划分为A分区和B分区，且分割线经过目标图像块的上边界和左边界，由此边界采样点包括边界采样点1和边界采样点2。
在一种实现方式中，可以包括以下步骤：根据边界映射表确定所述目标图像块使用的分割线所经过的边界；根据所述分割线所经过的边界确定所述边界采样点。
可选地，所述边界映射表包括各条分割线经过所述目标图像块中指定边界的映射关系，所述指定边界包括第一边界和/或第二边界。可选地，第一边界为上边界，第二边界为左边界。通过边界映射表可以确定目标图像块所使用的分割线经过目标图像块上边界和左边界的情况。如图8a所示的目标图像块中所使用的分割线同时经过上边界和左边界。
各条分割线经过所述目标图像块中指定边界的映射关系可以用于指示分割线的分割索引与分割线是否经过目标图像块的上边界和左边界。示例性地，如下表5所示的边界映射表。
表5边界映射表
此边界映射表是以几何划分模式中的64种划分模式分别对应的分割线索引，记录有每个分割线索引与该分割线是否经过左边界或者上边界的信息。由此，通过该边界映射表可以快速地确定出目标图像块使用的分割线所经过的边界，从而确定出边界采样点。
可选地，当所述目标图像块使用的分割线经过所述目标图像块的第一边界时，确定所述边界采样点包括第一边界采样点；和/或，当所述目标图像块使用的分割线经过所述目标图像块的第二边界时，确定所述边界采样点包括第二边界采样点。
通过边界映射表可以确定分割线经过指定边界的情况，进而确定边界采样点包括的内容。可选地，第一边界为上边界，第二边界为左边界。当分割线经过目标图像块的上边界时，边界采样点包括上边界采样点，和/或，当分割线经过目标图像块的左边界时，边界采样点包括左边界采样点。分割线可以经过上边界和左边界，也可以经过上边界或者左边界，边界采样点包括上边界采样点和/或左边界采样点。由此，在分割线经过的边界满足条件时(即经过的边界为第一边界和第二边界中的至少一者)，边界采样点包括至少一个。当分割线经过上边界时，分割线也可能同时经过左边界、右边界以及下边界中的任一者，当分割线经过左边界时，分割线也可能同时经过上边界、右边界以及下边界中的任一者。示例性地，如前述图4c所示的不同分割线在图像块中的分割情况。可以发现，有的分割线不经过图像块的左边界，有的分割线不经过上边界，有的分割线既不经过上边界也不经过左边界，例如第一行第4个图像块中的分割线。
在一个实施例中，对于边界采样点的确定方式，包括：根据所述目标图像块的目标划分模式参数确定分割线方程；根据边界参考点和所述分割线方程确定至少一所述边界采样点。
可选地，目标图像块的目标划分模式参数包括以下至少一项：角度索引、距离索引以及分割索引。该目标划分模式参数可以是几何划分模式参数。在一种实现方式中，根据目标划分模式参数包括的角度索引和距离索引确定分割线方程。分割线方程是指分割线的直线方程，根据角度索引angleIdx在角度映射表中确定分割线方程中的和并根据距离索引distanceIdx确定分割线方程中的ρ。对于分割线方程的表达式如下：
可选地，(xc，yc)为目标图像块中任一采样点的坐标。
在分割线方程确定之后，可以根据边界参考点和分割线方程确定至少一边界采样点。可选地，边界参考点是与目标图像块中编码顺序排列在首位的采样点。从位置上来看，边界参考点是目标图像块左上位置的采样点，在此将边界参考点的位置记为(xCb，yCb)。可选地，根据边界参考点确定至少一边界采样点的位置坐标。
至少一边界采样点包括第一边界采样点和/或第二边界采样点。若分割线经过第二边界(第二边界为左边界)，则边界采样点包括第二边界采样点，第二边界采样点的位置坐标为(x_gpm_left,y_gpm_left)；若分割线还经过第一边界(第一边界为上边界)则边界采样点还包括第一边界采样点，第一边界采样点的位置坐标为(x_gpm_above,y_gpm_above)。由于边界参考点的位置为(xCb，yCb)，而边界采样点和边界参考点存在相同坐标轴，因此，x_gpm_left＝xCb，y_gpm_above＝yCb，也即第二边界采样点与边界参考点同y轴，第一边界采样点与边界参考点同x轴，从而通过分割线方程可以确定出第二边界采样点的y_gpm_left以及第一边界采样点的x_gpm_above。若y_gpm_left的值超出预定范围(指在第二边界上排列在最后一个的临界采样点的y值)，则分割线不经过目标图像块的第二边界(例如左边界)，则第二边界采样点不存在，同理，若x_gpm_above的值超出预定范围(指在第一边界上排列在最后一个的临界采样点的x值)，则分割线不经过目标图像块的 第一边界(例如上边界)，则第一边界采样点不存在。示例性地，针对上述描述的内容，可以参阅如图8b所示的边界参考点和边界采样点之间的位置关系示意图。临界采样点1的y1可以作为第二边界采样点是否存在的衡量值，也就是说根据分割线求得的y_gpm_left与y1比较，若y_gpm_left大于y1，则第二边界采样点不存在，反之，则存在。对于临界采样点2，同理x2可以作为x_gpm_above是否超出预定范围的评判值，从而确定第一边界采样点是否存在。
需要说明的是，如果根据边界映射表确定出分割线所经过的边界，可以首先确定符合条件的边界采样点是否存在，进而按照上述方式获取对应边界采样点的位置坐标。举例来说，若根据分割线索引查询边界映射表，确定目标图像块使用的分割线经过上边界和左边界，则可以根据分割线方程和边界参考点确定上边界采样点和左边界采样点的具体位置。
在边界采样点确定之后，接着可以根据边界采样点和目标图像块对应分区的位置关系，确定出第一参考采样点和/或第二参考采样点。在一种实施方式中，可以包括如下步骤：根据所述至少一边界采样点确定第一坐标范围和/或第二坐标范围；根据所述第一坐标范围和/或所述第二坐标范围确定第一参考采样点和/或第二参考采样点。
可选地，至少一边界采样点包括第一边界采样点和/或第二边界采样点。在一实施方式中，包括以下至少一种方式：根据第一边界采样点和/或第二边界采样点确定第一坐标范围，根据第一边界采样和/或第二边界采样点确定第二坐标范围。以至少一边界采样点包括第一边界采样点和第二边界采样点，且第一边界采样点的位置坐标为(x_gpm_above,y_gpm_above)，第二边界采样点的位置坐标为(x_gpm_left,y_gpm_left)为例，对第一坐标范围和第二坐标范围进行示例性说明。第一坐标范围包括y＝y_gpm_above+1，x小于或等于x_gpm_above且大于或等于边界参考点的xCb的坐标范围，以及x＝x_gpm_left-1，y大于或等于y_gpm_left且小于或等于yCb的坐标范围；第二坐标范围包括y＝y_gpm_above+1，x大于x_gpm_above的坐标范围，以及x＝x_gpm_left-1，y小于y_gpm_left的坐标范围。从位置关系上来看，第一坐标范围内的参考采样点包括位于第二边界采样点左侧上方的参考采样点，以及位于第一边界采样点左侧上方的参考采样点；第二坐标范围的参考采样点包括位于第二边界采样点右侧上方的参考采样点，以及位于第一边界采样点左侧下方的参考采样点。
当目标图像块对应分区为使用第一预测模式(例如帧内预测模式)的第一分区时，可以将位于第一坐标范围内的参考采样点确定为第一参考采样点，和/或，将位于第二坐标范围内的参考采样点确定为第二参考采样点。当目标图像块对应分区为使用第一预测模式(例如帧内预测模式)的第二分区，且第二分区与第二坐标范围邻近时，可以将位于第二坐标范围内的参考采样点确定为第一参考采样点，和/或，将位于第一坐标范围内的参考采样点确定为第二参考采样点。
对于目标图像块对应分区来说，第一参考采样点和/或第二参考采样点存在可用或者不可用的情况。在本申请实施例中，第一参考采样点是指目标图像块对应分区可用的参考采样点，第二参考采样点是指目标图像块对应分区不可用的参考采样点。示例性地，如图8c所示，A分区采用帧内预测模式，对于上方参考采样点来说，由于位于gpm_above右侧的上方参考采样点的采样值和A分区中的原始采样值差异较大，若利用位于gpm_above右侧的上方参考采样点来预测A分区中的采样值，会使得得到的预测值和原始采样值之间的差异较大(即，残差较大)，从而使得编码质量不高，因此，位于gpm_above右侧的上方参考采样点均为对于A分区不可用的参考采样点。同理，对于左侧参考采样点来说，位于gpm_lef下方的左侧参考采样点均为对于A分区不可用的参考采样点。需要说明的是，对于A分区来说，可用的参考采样点为紧邻A分区的参考采样点。同理，当B分区采用帧内预测模式，对于上方参考采样点来说，位于gpm_above左侧的上方参考采样点均为对于B分区不可用的参考采样。对于左侧参考采样点来说，位于gpm_lef上方的左侧参考采样点均为对于B分区不可用的参考采样点。需要说明的是，对于B分区来说，可用的参考采样点为紧邻B分区的参考采样点。
对于第一参考采样点和第二参考采样点的确定方式，在另一个实施例中，S701的实现方式还可以是：根据所述目标图像块中对应分区的采样点与分割线之间的距离，确定所述目标图像块中对应分区的分区范围；基于第一采样点的坐标范围和所述分区范围确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；或者，基于与所述目标图像块相邻的第一采样点与所述分割线之间的距离信息，确定所述第一采样点为所述目标图像块中对应分区的第一参考采样点或者第二参考采样点。
此方式下，首先可以确定分割线方程，对于分割线方程的确定过程可以参见前述介绍，在此不做赘述。按照分割线方程可以确定目标图像块中对应分区的采样点与分割线之间的距离。假设目标图像块中对应分区的采样点为(xc，yc)，按照分割线方程可以得到采样点到分割线的距离，如下式所示的距离表达式。
d(xc，yc)为目标图像块中采样点到所使用的分割线之间的距离。采样点可以是指像素点。
目标图像块对应分区为采用第一预测模式(例如帧内预测模式)的分区。根据分区中采样点到分割线的距离，可以确定采样点是属于第一分区还是第二分区。例如，当设定属于第一分区的采样点到分割线之间的距离为正值，属于第二分区的采样点到分割线之间的距离为负值，例如当确定出采样点S与分割线之间的距离为正值时，则确定该采样点S属于第一分区。当确定出采样点S与分割线之间的距离为负值时，则确定该采样点S属于第二分区。需要说明的是，本发明并非以此为限。可以采用其他方式来确定采样点是属于第一分区还是第二分区。
在一实施例中，可以将与目标图像块的特定位置、或与目标图像块的相邻图像块的位置之间的距离小于预设距离的分区确定为第一分区，将与目标图像块的特定位置之间的距离大于预设距离的分区确定为第二分区。
在又一实施例中，可以将与目标图像块的相邻图像块的位置之间的距离小于预设距离的分区确定为第一分区，将与目标图像块的相邻图像块的位置之间的距离大于预设距离的分区确定为第二分区。
可选地，目标图像块的特定位置对应于目标图像块左上角采样点的坐标，目标图像块的相邻图像块的位置对应于构造的空域合并候选列表中目标图像块的空间相邻块的坐标。参见图6a，空间相邻块为上侧相邻块B1、左侧相邻块A1、右上侧相邻块B0、左下侧相邻块A0、左上侧相邻块B2。
第一采样点是与目标图像块相邻的参考采样点，第一采样点包括至少一个且第一采样点是已编码采样点。第一采样点可以作为第一参考采样点或者是第二参考采样点，可以采用以下两种方式中的任一种来确定：
①根据第一采样点的坐标范围和分区范围确定与目标图像块对应分区相邻或者不相邻的参考采样点。可以将与该对应分区相邻的第一采样点确定为对应分区的第一参考采样点，将与对应分区不相邻的第一采样点确定为对应分区的第二参考采样点。针对不同分区来说，第一参考采样点(或者是第二参考采样点)为可用或者是不可用参考采样点。示例性地，请参阅图8d，图8d是本申请实施例提供一种目标参考采样点的划分示意图。如图8d所示，图像块包括A分区和B分区，参考采样点包括与A分区相邻的参考采样点、与B分区相邻的参考采样点以及与A/B分区均不相邻的参考采样点。可选地，与A/B分区均不相邻的参考采样点是与目标图像块所在的编码树单元相邻的参考采样点。若目标图像块中使用帧内预测模式分区是A分区，则确定A分区对应的第一参考采样点为与A分区相邻的参考采样点，A分区对应的第二参考采样点为与B分区相邻的参考采样点和与A/B分区均不相邻的参考采样点。若目标图像块中使用帧内预测模式的分区是B分区，则确定B分区对应的第一参考采样点为与B分区相邻的参考采样点以及与A/B分区均不相邻的参考采样点，B分区对应的第二参考采样点为与A分区相邻的参考采样点。可见，第一参考采样点还包括与对应分区不相邻的参考采样点。
②根据与目标图像块相邻的第一采样点与分割线之间的距离信息来确定第一参考采样点是属于对应分区的第一参考采样点或者第二参考采样点。可选地，距离信息包括第一采样点与分割线之间的距离的符号。第一采样点是与目标图像块相邻的参考采样点。可以通过计算参考采样点到分割线的距离的符号来确定参考采样点是与第一分区相邻还是第二分区相邻。例如，若参考采样点到分割线的距离的符号与第一分区中的采样点到分割线的距离的符号是相同的，则该参考采样点与第一分区相邻。若参考采样点到分割线的距离的符号和第一分区中的采样到分割线的距离的符号是不相同的，则该参考采样点与第 一分区不相邻。此处距离的符号可以是用于刻画距离的正号和负号。
可选地，根据采用的第二预测模式的目标图像块对应分区确定该分区对应的第一参考采样和第二参考采样。第二预测模式为帧内预测模式。当第一分区采用帧内预测模式时，与第一分区相邻的参考采样点为第一分区的可用参考采样点，与第一分区不相邻的参考采样点为第一分区的不可用参考采样点。当第二分区采用帧内预测模式时，与第一分区不相邻的参考采样点为第二分区的可用参考采样点，与第一分区相邻的参考采样点为第二分区的不可用参考采样点。
S702，根据所述第一参考采样点和/或所述第二参考采样点确定所述目标图像块中对应分区的预测结果集合。
若通过上述方式将第一参考采样点和第二参考采样点确定出来，则可以进一步使用两种参考采样点进行帧内预测，得到目标图像块的预测结果集合。虽然其中一种参考采样点对于某个分区来说是不可用的，但是可以通过相应地处理使其应用于帧内预测的实际预测过程中，并且保证编码质量，这样目标图像块在划分为不同的图像区域进行预测时，可以灵活地从不同种类的第二预测模式中选择，当第二预测模式为帧内预测模式时，对于多种帧内预测模式候选中的任一种均可以被选择使用，从而使得用于划分模式的帧内预测模式的种类的选择范围得到了充分地扩展，保证编码质量的同时提高选择的灵活度以及范围。
在一个实施例中，步骤S702包括以下内容：(1)根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；(2)根据所述第一参考采样点和所述填充后的参考采样点确定所述目标图像块对应分区的预测结果集合。
由于第一参考采样点对于目标图像块对应分区来说是可用的参考采样点，而第二参考采样点对于目标图像块对应分区来说是不可用的参考采样点，通过利用可用的参考采样点填充不可用的参考采样点，可使得不可用的参考采样点被其他像素代替，得到填充后的参考采样点，从而将第二参考采样点成为实际处理过程中可用的参考采样点来使用，即：将第一参考采样点和填充后的参考采样点作为最终的参考采样点来进行预测，得到预测结果集合。这样目标图像块对应分区的预测结果集合的确定过程由第一参考采样点和填充后的参考采样点共同参与或作用。
下面对第二参考采样点的填充方式进行详细地介绍。在一种实施方式中，包括以下步骤S7021～S7023：
S7021：在所述第一参考采样点中确定至少一个填充参考采样点。
对第二参考采样点进行填充，首先需要确定可靠的填充参考采样点。由于第一参考采样点对于目标图像块对应分区来说是可用的参考采样点，通过利用可用的参考采样点作为填充参考采样点可以有效降低第二参考采样点与对应分区中采样点之间的差异。至少一个填充参考采样点可以是邻近的一个或者至少一个第一参考采样点，例如与第二参考采样点最邻近的第一参考采样点，或者是与第二参考采样点邻近的顺序排列的至少一个第一参考采样点。至少一个填充参考采样点也可以第一参考采样点中的任意一个或者多个，例如第一参考采样点中的。
需要说明的是，由于第一参考采样点和第二参考采样点对于目标图像块的不同分区来说所包括的采样点是不同的，由此，至少一个填充参考采样点对于目标图像块的不同分区来说也是不同的。
示例性地，请参阅图9a和图9b，图9a和图9b是本申请实施例提供的一些填充参考采样点的示意图。如图9a所示，A分区为目标图像块中使用帧内预测模式的分区，阴影区域的参考采样点中，与A分区相邻的参考采样点为A分区可用参考采样点，与A分区不相邻的参考采样点为A分区不可用参考采样点。至少一个填充参考采样点包括A分区可用的参考采样点中的两个可用的最邻近参考采样点。如图9b所示，B分区为目标图像块中使用帧内预测模式的分区，阴影区域的参考采样点中，与A分区不相邻的参考采样点为B分区可用参考采样点，与A分区相邻的参考采样点为B分区不可用参考采样点。至少一个填充参考采样点包括B分区可用参考采样点中的两个可用的最邻近参考采样点。
S7022：基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值。
当至少一个填充参考采样点为最邻近的第一参考采样点时，可以将最邻近的第一参考采样点的采样值作为第二参考采样点的填充值，当至少一个填充参考采样点包括邻近的至少一个第一参考采样点时，可以基于邻近的至少一个第一参考采样点确定第二参考采样点的填充值。例如，将邻近的至少一个第一参考采样点的平均值作为第二参考采样点的填充值。
在一个实施例中，可以为至少一个填充参考采样点赋予不同的权重，这样可以考虑到不同位置的填充参考采样点对于第二参考采样点的填充值的影响。可选地：基于各个填充参考采样点与所述第二参考采样点之间的位置关系确定各个填充参考采样点的填充权重；基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值。
各个填充参考采样点即至少一个填充参考采样点中的每一个填充参考采样点。由此各个填充参考采样点都对应有填充权重。例如3个填充参考采样点，分别对应有3个填充权重，基于填充权重可以对各个填充参考采样点的采样值进行加权求和，得到的加权求和值即可以作为第二参考采样点的填充值。
可选地，位置关系包括所述各个填充参考采样点和所述第二参考采样点之间的距离。此处的距离可以是指两个参考采样点之间的直线距离，可以基于填充参考采样点和第二参考采样点之间的距离远近确定填充权重的大小，由于第二参考采样点与距离越远的填充参考采样点之间的相关性越小，因此距离越远的填充参考采样点，所设置的填充权重可以越小，这样填充权重随着距离的增加而减小。当存在多个填充参考采样点时，可以使得相关性较强的填充参考采样点在调整第二参考采样点和分区中采样点之间的差值过程中发挥的作用更大，获得较佳的填充质量，从而更好地减小残差，提高编码质量。
可选地，第一参考采样点包括与所述目标图像块的第一边界相邻的第一参考采样点，和/或与所述目标图像块的第二边界相邻的第一参考采样点。
可选地，第一边界为上边界，第二边界为左边界。可以依据与目标图像块边界的相邻情况将第一参考采样点划分为两类，即与上边界相邻的第一参考采样点以及与左边界相邻的第一参考采样点。此外，还可能包括与上边界和左边界均不相邻的第一参考采样点。示例性地，如图9b中B分区可用参考采样点(即第一参考采样点)包括与A、B分区均不相邻的参考采样点。
由于填充参考采样点是从第一参考采样点中确定的，可选地：所述至少一个填充参考采样点包括与所述第一边界相邻的第一参考采样点中的第一填充参考采样点，和/或与所述第二边界相邻的第一参考采样点中的第二填充参考采样点。也就是说，至少一个填充参考采样点包括第一填充参考采样点和第二填充参考采样点，第一填充参考采样点是与上边界相邻的第一参考采样点中的一个或者多个，第二填充参考采样点是与左边界相邻的第一参考采样点中的一个或者多个。
按照上述对第一参考采样点和至少一填充参考采样点的划分，对于第二参考采样点的填充值的确定方式，可选地，可以为：基于第一填充参考采样点的采样值和第一填充参考采样点的填充权重，和/或，第二填充参考采样点的采样值和第二填充参考采样点的填充权重，确定与对应边界相邻的所述第二参考采样点的填充值。
对应边界包括目标图像块的第一边界和/或第二边界，可选地，第一边界为上边界，第二边界左边界。也就是说，对于第二参考采样点的填充值确定包括以下几种情况中的任一种或者多种：基于第一填充参考采样点的采样值和第一填充参考采样点的填充权重确定与第一边界相邻的第二参考采样点的填充值；基于第二填充参考采样点的采样值和第二填充参考采样点的填充权重确定与第二边界相邻的第二参考采样点的填充值；基于第一填充参考采样点的采样值和第一填充参考采样点的填充权重，以及第二填充参考采样点的采样值和第二填充参考采样点的填充权重，确定所有第二参考采样点的填充值，所有第二参考采样点包括与第一边界相邻的第二参考采样点，和/或与第二边界相邻的第二参考采样。
示例性地，以B分区为目标图像块中使用帧内预测模式的分区为例进行说明，如图9c所示，至少一个填充参考采样点包括第一填充参考采样点和第二填充参考采样点，第一填充参考采样点包括与B分区上边界相邻的可用参考采样点中的3个相邻的可用参考采样点(可用参考采样点即第一参考采样点)，以及与B分区左边界相邻的可用参考采样点中的3个相邻的可用参考采样点，这些填充采样点均与不可用参考采样点(即第二参考采样点)相邻。第一填充参考采样点可以用于填充与A分区上边界相邻的第一参考采样点，且与A分区上边界相邻的各个第一参考采样点的填充值均相同。第二填充参考 采样点可以用于填充与A分区左边界相邻的第一参考采样点，与A分区左边界相邻的各个第一参考采样点的填充值均相同。
如图9d所示，至少一个填充参考采样点也包括第一填充参考采样点与第二填充参考采样点；第一填充参考采样点包括彼此互不相邻、且与上边界相邻的可用参考采样点，第二填充参考采样点包括彼此互不相邻、且与左边界相邻的可用参考采样点。可以根据第一填充参考采样点对同一边界上的第二参考采样点进行填充：根据填充权重与该第一填充参考采样点的采样值进行加权求和得到填充值，来填充第二参考采样点。最终得到的该边界上填充后的第二参考采样点的采样值是相同的。在另一种实现方式中，还可以是直接将第一填充参考采样点的采样值作为填充值，按照预设方向交替填充。例如第一填充参考采样点包括采样点1、2、3，那么可以将采样点1的采样值填充至从右往左的第一个第二参考采样点，采样点2的采样值填充从右往左的第二个第二参考采样点，采样点2的采样值填充从右往左的第三个第二参考采样点，采样点1接着填充从右往左的第四个第二参考采样点，依次循环，直至将与上边界相邻的第二参考采样点填充完毕。
根据第一填充参考采样点与第二填充参考采样点填充第二参考采样点的示意图可以参阅图9e。如图9e中的(1)所示，由于第一填充参考采样点和第二填充参考采样与待填充的第二参考采样点之间的距离相等，两个填充参考采样点所设置的填充权重相同，因此，第二参考采样点的填充值也是第一填充参考采样点和第二填充参考采样点之间的平均值。如图9e中的(2)所示，第一填充参考采样点和第二填充参考采样点分别与待填充的第二参考采样点之间的距离不同，设定的填充权重也不同，在计算填充值时第一填充参考采样点的占比将更大。
S7023：基于所述填充值对所述第二参考采样点进行填充，得到填充后的参考采样点。
基于填充值对第二参考采样点的填充可以是赋值的过程，例如将最邻近的第一参考采样点的采样值赋值给第二参考点，或者是将邻近的至少一个第一参考采样点的平均采样值赋值给第二参考采样点，得到填充后的参考采样点。填充后的参考采样点中的采样值即该填充值。
在得到填充后的参考采样点之后，可以根据第一参考采样点和所述填充后的参考采样点确定目标图像块对应分区的预测结果集合。这样，在确定目标图像块对应分区的预测结果集合时，即使存在不可用的参考采样点(即第二参考采样点)，通过将不可用的参考采样点进行处理，相比于使用不做任何处于的第二参考点进行预测的情况下，预测结果的不理想情况将得到有效改善。
需要说明的是，本申请实施例提供的方案还可以应用于非方形的编码块，由于非方形的编码块采用的是广角帧内预测模式，在编码块采用GPM模式时，广角帧内预测模式还可以与GPM模式相结合，从而扩展了与GPM模式相结合的帧内预测模式的种类。对于广角帧内预测模式如图9f所示。可选地，虚线箭头所示的帧内预测方向对应的帧内预测模式为广角帧内预测模式。
综上所述，本申请实施例提供的图像处理方案在使用第二预测模式(如帧内预测模式)预测目标图像块时，可以通过确定使用第二预测模式的分区对应的第一参考采样点和/或第二参考采样点，这些参考采样点中包括可用的参考采样点和不可用的参考采样点，通过可用的参考采样点对不可用的参考采样点进行填充，可以减低原本不可用的参考采样点和使用第二预测模式的分区中采样点之间的原始值的差异，在实际使用过程中，将原本不可用的参考采样点变为可用的参考采样点，在对图像块进行预测过程中使用，从而得到较小的残差，提升编码质量。这样原本第二预测模式不佳的处理效果得到了有效地改善，任何类型的第二预测模式均不必受限于预测效果不佳的情景而不选择，从而可以在多种类型的第二预测模式中选择合适的类型的第二预测模式，预测模式的选择范围得到了增大，灵活性更高，还能够保证编码质量。当目标图像块在GPM模式下可用的帧内预测模式通过编码质量的保证可以得到有效地扩展，GPM模式与帧内预测模式的组合灵活性更强，由此在灵活性和编码质量之间达到较好的平衡。
第四实施例
请参阅图10，图10是根据第四实施例示出的一种图像处理方法的流程示意图，该实施例中的执行主体可以是一个计算机设备或者是多个计算机设备构成的集群，该计算机设备可以是智能终端(如前述移动终端100)，也可以是服务器，此处，以本实施例中的执行主体为智能终端为例进行说明。
S1001，通过预设策略确定目标参考采样点。
在一个实施例中，步骤S1001的可选实现方式可以为：通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点。
图像块是指输入视频图像(即视频帧)中当前正在编码的图像块，可简称为当前块或者当前图像块或者当前编码块。此处的图像块与前述实施例中提及的目标图像块对应，图像块可以是目标图像块，相关介绍可以参考第一实施例，在此不做赘述。
可选地，图像块分区是由图像块使用的分割线划分的图像区域，图像块分区包括第一分区和/或第二分区，图像块分区中采样点包括第一分区的采样点和/或第二分区的采样点。参考采样点包括与图像块分区相邻的已编码采样点和/或与图像块所在参考图像块相邻的已编码采样点，当图像块为编码单元时，参考图像块为编码树单元。参考图像块中包括至少一个图像块，当至少一个图像块依次编码完成，则参考图像块的编码完成，如上述图6d所示。
参考采样点与图像块分区中采样点的位置关系可以是相对的方位关系，也可以通过距离信息来刻画。可以基于参考采样点与图像块分区的采样点的位置关系确定参考采样点对于该图像块分区是可用或者不可用，进而确定出目标参考采样点。目标参考采样点可以包括图像块分区可用的参考采样点和/或图像块分区不可用的参考采样点。
在一种实现方式中，通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点可以包括以下步骤1)和2)：
1)从图像块分区的采样点中确定至少一边界采样点。
可选地，所述至少一边界采样点包括第一边界采样点和/或第二边界采样点，当图像块中分割线经过图像块的第一边界时，至少一边界采样点包括第一边界采样点，当分割线经过图像块的第二边界时，至少一边界采样点包括第二边界采样点，当分割线同时经过图像块的第一边界和第二边界时，至少一边界采样点包括第一边界采样点和第二边界采样点。可选地，第一边界为上边界，第二边界为左边界，第一边界采样点为上边界采样点，第二边界采样点为左边界采样点。示例性地，对于第一边界采样点和第二边界采样点可以参见前述实施例中图8a。
在一个实施例中，至少一边界采样点的确定方式包括：根据图像块的目标划分模式参数确定分割线方程；根据边界参考点和所述分割线方程确定至少一所述边界采样点。此方式的具体内容可以参见第三实施例中的相关内容，在此不做赘述。
2)根据参考采样点与所述至少一边界采样点之间的位置关系确定目标参考采样点。
在一个实施例中，至少一边界采样点包括第一边界采样点和/或第二边界采样点。步骤2)的可选实现方式包括：根据所述第一边界采样点确定第一坐标范围，和/或，根据所述第二边界采样点确定第二坐标范围；根据参考采样点与所述第一坐标范围之间的位置关系，和/或，根据所参考采样点与所述第二坐标范围之间的位置关系，确定目标参考采样点。
第一坐标范围和/或第二坐标范围用于划定目标参考采样点的采样区域，可以将第一边界采样点上方左侧的参考采样点所在的区域，和/或第二边界采样点左侧上方点的参考采样点所在的区域确定为第一坐标范围，将第一边界采样点上方右侧的参考采样点所在的区域，和/或第二边界采样点左侧下方的参考采样点所在的区域确定为第二坐标范围。
将在第一坐标范围内的参考采样点，和/或第二坐标范围内的参考采样点确定为目标参考采样点。可选地，目标参考采样点包括第一参考采样点和/或第二参考采样点，对于图像块不同分区，目标参考采样点的采样值有所不同。当图像块分区为第一分区，且第一分区与第一坐标范围邻近，则可以将在第一坐标范围内的参考采样点作为第一参考采样点，将在第二坐标范围内的参考采样点作为第二参考采样点。
在另一种实现方式中，通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点可以包括以下步骤：根据图像块分区中采样点确定分区范围；根据参考采样点和所述分区范围的位置关系确定目标参考采样点。
可以根据图像块分区中边界上的采样点划定分区范围，例如当图像块使用的分割线经过指定边界(包括第一边界和/或第二边界)时，可以根据分割线在图像块中的边界采样点和指定边界上的临界采样点确定分区范围，临界采样点是指图像块最边缘的采样点，如上述图8b中包含的临界采样点。根据边界采样点和临界采样点的不同位置坐标可以确定沿该指定边界的分区范围的范围值。
参考采样点与分区范围的位置关系可以用于描述参考采样点和图像块分区是否相邻或者是距离的远近。由此，确定目标参考采样点可以包括以下方式：根据参考采样点的坐标范围和所述分区范围确定目标参考采样点，或者，根据参考采样点与图像块分区所使用的分割线之间的距离信息，确定目标参考采样点。此处的参考采样点可以对应于前述的第一采样点，该参考采样点包括与目标图像块相邻的参考采样点，可选地，还可以包括与目标图像块不相邻的参考采样点。目标参考采样点包括第一参考采样点和/或第二参考采样点，可与图像块分区相邻或者与图像块分区不相邻。详细内容介绍可以参见前述第三实施例中的相关内容，在此不做赘述。
可选地，所述目标参考采样点包括第一参考采样点和/或第二参考采样点；
所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
以上详细内容介绍可以参见前述第三实施例中的相关内容，在此不做赘述。
S1002，根据所述目标参考采样点和预设预测模式，确定预测结果集合。
在目标参考采样点确定之后，可以基于该目标参考采样点和预设预测模式确定图像块的预测结果集合。所述预测结果集合用于确定图像块的预测结果。可选地，所述预测结果集合用于确定图像块的预测结果。所述预测结果集合包括第一预测结果集合和/或第二预测结果集合。第一预测结果集合和/或第二预测结果集合可以根据图像块不同分区的所使用的预设预测模式得到，基于第一预测结果集合和/或第二预测结果集合确定图像块的预测结果可以参见前述实施例的介绍，在此不做赘述。对于预测结果集合的确定方式，更详细地可以参见下述实施例，在此先不详述。
本实施例提供的图像处理方案，通过预设策略确定目标参考采样点，预测策略可以是指参考采样点和图像块分区采样点之间的位置关系是否满足条件，在满足条件时，例如参考采样点与图像块对应分区相邻，对于该分区来说即为目标参考采样点。通过目标参考采样点可以将对于图像块分区可用或者不可用的参考采样点确定出来，并在预设预测模式下，根据目标参考采样点得到准确的预测结果集合，进而提高预测结果的质量。
第五实施例
请参阅图11，图11是根据第五实施例示出的一种图像处理方法的流程示意图，该实施例中的执行主体可以是一个计算机设备或者是多个计算机设备构成的集群，该计算机设备可以是智能终端(如前述移动终端100)，也可以是服务器，此处，以本实施例中的执行主体为智能终端为例进行说明。
在一个实施例中，可以根据图像块的预测模式指示信息确定预设预测模式，并根据所述预设预测模式和所述目标参考采样点，确定预测结果集合。
可选地，图像块的预测模式指示信息包括用于指示所述第一分区所使用的预测模式的预测模式指示信息，和/或，用于指示所述第二分区所使用的预测模式的预测模式指示信息。这些预测模式指示信息可以是目标图像块对应分区在编码端使用的预测模式类型的标记或者索引，可以在编码端或者是解码端使用。
预设预测模式为：图像块中分割线划分的分区所使用的预测模式和/或相邻图像块所使用的预测模式。可选地，目标图像块中分割线划分的分区所使用的预测模式包括以下任一项：相邻图像块所使用的预测模式，至少一个相邻图像块所使用的预测模式中使用次数大于或等于预设阈值的预测模式。
在一实施例中，可将相邻图像块所使用的预测模式作为目标图像块中分割线划分的分区所使用的预测模式。可选地，可将至少一个相邻图像块所使用的预测模式中使用次数最多或次数较多的预测模式作为目标图像中分割线划分的分区所使用的预测模式。在另一实施方式中，可以通过其他方式得到目标图像中分割线划分的分区所使用的预测模式。在上述实施方式中，无需通过计算率失真代价来确定采用的预设预测模式。然而，本发明并非限于此，在上述实施方式中，也可以通过计算率失真代价来确定预设预测模式。
由于图像块分割线划分的分区包括第一分区和/或第二分区，预设预测模式可以包括第一分区所使用的预测模式和/或第二分区所使用的预测模式，且预测模式包括第一预测模式和/或第二预测模式，例如，图像块分区包括第一分区和第二分区，则存在以下情况中的任一种：两个分区都使用第一预测模式；两个分区均使用第二预测模式；一个分区使用第一预测模式，另一个分区使用第二预测模式。相邻图像块所使用的预测模式可以作为当前正在编码的图像块的参考，例如相邻图像块使用第一预测模式，则当前编码的图像块也可以直接使用该第一预测模式。
可选地，预测模式为图像块中分割线划分的分区所使用的预测模式，根据预设预测模式和目标参考采样点确定预测结果集合的详细内容可以参见S1101和S1102的介绍。
S1101，若所述预测模式包括第一预测模式和/或第二预测模式，则根据运动矢量和/或目标参考采样点，确定图像块对应分区的预测结果集合。
也就是说，图像块中分割线划分的分区使用的预测模式可以为第一预测模式和/或第二预测模式，对于整个图像块来说，预设预测模式可以按照第一预测模式进行预测，也可以按照第二预测模式进行预测，还可以在不同分区按照所使用第一预测模式和第二预测模式进行预测，此时第一预测模式和第二预测模式用于图像块不同分区，如前述提及的一个分区使用第一预测模式，另一个分区使用第二预测模式。
在一个实施例中，若所述预测模式包括第二预测模式，则根据所述目标参考采样点确定图像块对应分区的预测结果集合。可选地，第二预测模式为帧内预测模式。
可选地，目标参考采样点包括第一参考采样点和第二参考采样点，根据所述目标参考采样点确定图像块对应分区的预测结果集合，包括：根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；根据所述第一参考采样点和所述填充后的参考采样点确定所述图像块对应分区的预测结果集合。
由于第二参考采样点对于使用第二预测模式的图像块对应分区来说是不可用的，而第一参考采样点对于图像块分区对于使用第二预测模式的图像块对应分区来说是可用的，利用可用的参考采样点填充不可用的参考采样点，并基于填充后的参考采样点和可用的参考采样点一同确定图像块对应分区的预测结果集合，可以减小不可用的参考采样点在实际使用中对预测结果所带来的误差。
在一种实施方式中，针对第二参考采样点的填充方式可以包括：在所述第一参考采样点中确定至少一个填充参考采样点；基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值；基于所述填充值对所述第二参考采样点进行填充，得到填充后的参考采样点。
可选地，针对不同的填充参考采样可以引入填充权重来确定填充值：基于所述至少一个填充参考采样点中的每一个与所 述第二参考采样点之间的位置关系确定各个填充参考采样点的填充权重；基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值。
可选地，填充权重随着距离的增加而减小。当存在多个填充参考采样点时，通过填充权重可以使得相关性较强的填充参考采样点，在调整第二参考采样点和分区中采样点之间的差值的过程中发挥的作用更大，获得较佳的填充质量，从而更好地减小残差，提高编码质量。
可选地：所述位置关系包括所述至少一个填充参考采样点中的每一个和所述第二参考采样点之间的距离；所述第一参考采样点包括与所述目标图像块的第一边界相邻的第一参考采样点，和/或与所述目标图像块的第二边界相邻的第一参考采样点；所述至少一个填充参考采样点包括所述与所述第一边界相邻的第一参考采样点中的第一填充参考采样点，和/或所述与所述第二边界相邻的第一参考采样点中的第二填充参考采样点。
所述基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值，包括：基于所述第一填充参考采样点的采样值和所述第一填充参考采样点的填充权重，和/或，所述第二填充参考采样点的采样值和所述第二填充参考采样点的填充权重，确定与对应边界相邻的所述第二参考采样点的填充值。针对上述示出的填充步骤可以参考第三实施例中介绍的相关内容，在此不做赘述。
在另一个实施例中，若所述预测模式包括第一预测模式，则根据运动矢量确定图像块对应分区的预测结果集合。可选地，根据第一预测模式的类型的不同，运动矢量包括第一运行矢量和/或第二运动矢量。第一预测模式的类型包括双向预测模式和/或单向预测模式，当第一预测模式为双向预测模式时，可以根据第一运动矢量和第二运动矢量确定图像块对应分区的预测结果集合，当第一预测模式的类型为单向预测模式时，可以根据第一运动矢量或者第二运动矢量确定图像块对应分区的预测结果集合。详细内容可以参见前述实施例的介绍，在此不做赘述。
在又一个实施例中，若所述预测模式包括第一预测模式和第二预测模式，则根据运动矢量确定图像块对应分区的预测结果集合，以及根据目标参考采样点确定图像块对应分区的预测结果集合。此处使用运动矢量是指使用第一预测模式的图像块分区的运动矢量，目标参考采样点是使用第二预测模式的图像块分区对应的参考采样点。且使用第一预测模式的图像块分区和使用第二预测模式的图像块分区不同。在此实施方式下详细地实现方式可以分别按照前述介绍的内容，在此不做赘述。
S1102，根据所述图像块对应分区的预测结果集合确定所述图像块的预测结果集合。
图像块对应分区的预测结果集合包括第一分区的第一预测结果集合和/或第二分区的第二预测结果集合，第一预测结果集合可以是通过第一预测模式或者第二预测模式得到的，同理，第二预测结果集合也可以是通过第一预测模式或者第二预测模式得到的。
举例来说，如图8c所示的图像块包括A分区和B分区，第一预测模式为帧间预测模式，第二预测模式为帧内预测模式。当A分区和B分区中的一个分区采用帧内预测，另一分区采用帧间预测时，确定当前块的第一预测结果集合和第二预测结果集合的处理包括以下内容：针对采用帧内预测的分区，在确定分区的目标参考采样点之后，利用具体使用的帧内预测模式类型，确定当前块的帧内预测值，得到第一预测值集合，该第一预测值集合是一个关于帧内预测的预测值集合，视对应使用帧内预测模式的分区可以将该第一预测值集合作为第一预测结果集合或者第二预测结果集合，例如A分区使用帧内预测模式，则可以作为第一预测结果集合。需要说明的是，若A分区和B分区中仅一个分区进行帧内预测，则仅得到一个关于帧内预测的预测值集合。若A分区和B分区中存在一个分区，该分区使用帧间预测，则通过空域合并候选列表得到该分区对应的帧间预测运动矢量，之后，确定当前块关于该帧间预测运动矢量的帧间预测值，从而得到第二预测值集合，该第一预测值集合是一个关于帧内预测的预测值集合，视对应使用帧内预测模式的分区可以将该第一预测值集合作为第一预测结果集合或者第二预测结果集合，例如A分区使用帧内预测模式，则可以作为第一预测结果集合。需要说明的是，对于使用帧间预测的分区来说，除了通过空域合并候选列表得到该分区对应的帧间预测模式下的运动矢量，也可以利用分区的目标参考采样点来确定该分区对应的帧间预测模式下的运动矢量。在一实施方式中，若B分区使用帧间预测模式，并且对于B分区可用的参考采样点(例如，图6c中的第一参考采样点)中存在一个参考采样点x使用帧间预测，则可以利用该参考采样点x的运动矢量确定B分区对应的运动矢量。可选地，可以直接将参考采样点x的运动矢量作为B分区的对应的帧间预测运动矢量。可选地，参考采样点x为位于可用的参考采样点中的中心或中间的参考采样点。
当A分区和B分区均进行帧内预测时，确定当前块的第一预测值集合和第二预测值集合的处理包括以下内容：若A分区和B分区均进行帧内预测，则分别确定对于A分区和B分区各自的参考采样点和不可用的参考采样点。在将利用各自最邻近的可用的参考采样点对各自不可用的参考采样点进行填充之后，分别利用各分区确定的帧内预测模式类型和各自的最终的参考采样点，确定当前块关于各分区确定的帧内预测模式类型对应的帧内预测值，从而得到第一预测结果集合和第二预测结果集合。可选地，该第一预测结果集合和第二预测结果集合均是通过帧内预测得到的，第一预测结果集合和第二预测结果集合可以是关于帧内预测的预测值集合。
需要说明的是，第二预测模式为帧内预测模式，通过上述填充操作，不仅可以使得帧内预测模式中包含的角度预测模式与划分模式(例如GPM预测模式)相结合，还可以使得广角帧内预测模式与划分模式相结合，这样扩展了可以与划分模式相结合的第二预测模式的种类，即包括用于方形的图像块的帧内预测模式以及用于非方形的图像块的帧内预测模式，由此也提高了不同尺寸的图像块的适用性。
本申请实施例提供的图像处理方案，由于不可用的参考采样点的采样值可被其他像素(此处为填充值)所代替，对于一分区不可用的参考采样点，通过将这些不可用的参考采样基于可用参考采样点的采样值赋予新的采样值，可以使得这些参考采样点的采样值和进行第二预测(例如帧内预测)的分区中采样点的原始值差异较小，因而对应得到的残差较小，编码质量较高。同时，可用于与图像块的划分模式(例如GPM模式)相结合的第二预测模式(例如帧内预测模式)的种类得到了扩展，从而使得在划分模式下按照第二预测模式进行预测的灵活性和预测结果的质量得到了提高。
请参见图12，图12是本申请实施例示出的一种图像处理装置的结构示意图，该图像处理装置可以是运行于服务器中的一个计算机程序(包括程序代码)，例如图像处理装置为一个应用软件；该装置可以用于执行本申请实施例提供的方法中的相应步骤。该图像处理装置1200包括：确定模块1201和填充模块1202。
确定模块1201，用于根据预设预测模式确定目标图像块中对应分区的预测结果集合，所述预测结果集合用于确定所述目标图像块的预测结果。
可选地，所述目标图像块包括第一分区和/或第二分区，所述第一分区和/或第二分区是由分割线划分得到的图像区域；所述预设预测模式包括所述目标图像块中分割线划分的分区所使用的预测模式；所述预测结果集合包括所述第一分区的第一预测结果集合和/或所述第二分区的第二预测结果集合。
在一个实施例中，确定模块1201还用于：根据第一划分模式集合确定目标图像块的目标划分模式参数；所述目标划分模式参数包括用于指示所述目标图像块中对应分区所使用的预测模式的预测模式指示信息。
在一个实施例中，确定模块1201，具体用于以下至少一项：若所述预测模式为第一预测模式，则根据所述目标图像块中对应分区的运动矢量确定所述目标图像块中对应分区的预测结果集合；若所述预测模式为第二预测模式，则根据所述目标图像块中对应分区的目标参考采样点确定所述目标图像块中对应分区的预测结果集合。
在一个实施例中，确定模块1201，具体用于：根据所述目标图像块的合并候选列表确定所述目标图像块中对应分区的第一运动矢量和/或第二运动矢量；根据所述第一运动矢量和/或所述第二运动矢量确定所述目标图像块中对应分区的预测结果集合。
可选地，所述目标参考采样点包括第一参考采样点和/或第二参考采样点。
可选地，包括以下至少一项：
所述第一参考采样点和所述第二参考采样点不同；
所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
可选地，所述第二预测模式包括至少一种类型的第二预测模式，若所述预测模式为第二预测模式，则确定模块1201，还用于：从所述至少一种类型的第二预测模式中确定所述目标图像块对应分区所使用的目标类型的第二预测模式，所述目标类型的第二预测模式用于确定所述目标图像块的预测结果集合。
在一个实施例中，确定模块1201，具体用于：确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；根据所述第一参考采样点和/或所述第二参考采样点确定所述目标图像块中对应分区的预测结果集合。
在一个实施例中，确定模块1201，具体用于：根据所述目标图像块中对应分区与所述目标图像块使用的分割线在所述目标图像块中的边界采样点之间的位置关系，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点。
在一个实施例中，确定模块1201，具体用于：根据边界映射表确定所述目标图像块使用的分割线所经过的边界；根据所述分割线所经过的边界确定所述边界采样点。
在一个实施例中，确定模块1201，具体用于以下至少一项：当所述目标图像块使用的分割线经过所述目标图像块的第一边界时，确定所述边界采样点包括第一边界采样点；当所述目标图像块使用的分割线经过所述目标图像块的第二边界时，确定所述边界采样点包括第二边界采样点。
可选地，确定模块1201具体还用于根据所述目标图像块的目标划分模式参数确定分割线方程；根据边界参考点和所述分割线方程确定至少一所述边界采样点。
在一个实施例中，确定模块1201，具体用于：根据所述至少一边界采样点确定第一坐标范围和/或第二坐标范围；根据所述第一坐标范围和/或所述第二坐标范围确定第一参考采样点和/或第二参考采样点。
在一个实施例中，确定模块1201，具体用于：根据所述目标图像块中对应分区的采样点与分割线之间的距离，确定所述目标图像块中对应分区的分区范围；基于第一采样点的坐标范围和所述分区范围确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；或者，基于与所述目标图像块相邻的第一采样点与所述分割线之间的距离信息，确定所述第一采样点为所述目标图像块中对应分区的第一参考采样点或者第二参考采样点。
在一个实施例中，填充模块1202，用于：根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；根据所述第一参考采样点和所述填充后的参考采样点确定所述目标图像块对应分区的预测结果集合。
在一个实施例中，填充模块1202，具体用于：在所述第一参考采样点中确定至少一个填充参考采样点；
基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值；基于所述填充值对所述第二参考采样点进行填充，得到填充后的参考采样点。
在一个实施例中，填充模块1202，具体用于：基于各个填充参考采样点与所述第二参考采样点之间的位置关系确定所述各个填充参考采样点的填充权重；基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值。
可选地，包括以下至少一项：
所述位置关系包括所述各个填充参考采样点和所述第二参考采样点之间的距离；
所述第一参考采样点包括与所述目标图像块的第一边界相邻的第一参考采样点，和/或与所述目标图像块的第二边界相邻的第一参考采样点；
所述至少一个填充参考采样点包括与所述第一边界相邻的第一参考采样点中的第一填充参考采样点，和/或与所述第二边界相邻的第一参考采样点中的第二填充参考采样点；
在一个实施例中，填充模块1202，具体用于：基于第一填充参考采样点的采样值和第一填充参考采样点的填充权重，和/或，第二填充参考采样点的采样值和第二填充参考采样点的填充权重，确定与对应边界相邻的所述第二参考采样点的填充值。
在一种可能的实施方式中，如图12所示的图像处理装置1200也可以用于下述图像处理方法所包括的内容。
确定模块1201，用于通过预设策略确定目标参考采样点；
确定模块1201，还用于根据所述目标参考采样点和预设预测模式，确定预测结果集合。
在一个实施例中，确定模块1201，具体用于：通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点。
在一个实施例中，确定模块1201，具体用于：从图像块分区的采样点中确定至少一边界采样点；根据参考采样点与所述至少一边界采样点之间的位置关系确定目标参考采样点。
可选地，所述至少一边界采样点包括第一边界采样点和/或第二边界采样点，确定模块1201，具体用于：根据所述第一边界采样点确定第一坐标范围，和/或，根据所述第二边界采样点确定第二坐标范围；
根据参考采样点与所述第一坐标范围之间的位置关系，和/或，根据所参考采样点与所述第二坐标范围之间的位置关系，确定目标参考采样点。
在一个实施例中，确定模块1201，具体用于：根据图像块分区中采样点确定分区范围；根据参考采样点和所述分区范围的位置关系确定目标参考采样点。
在一个实施例中，确定模块1201，具体用于：根据图像块的预测模式指示信息确定预设预测模式；根据所述预设预测模式和所述目标参考采样点，确定预测结果集合。
可选地，所述预设预测模式为以下至少一项：图像块中分割线划分的分区所使用的预测模式和/或相邻图像块所使用的预测模式。
在一个实施例中，确定模块1201，具体用于：若所述预测模式包括第一预测模式和/或第二预测模式，则根据运动矢量和/或目标参考采样点，确定图像块对应分区的预测结果集合；根据所述图像块对应分区的预测结果集合确定所述图像块的预测结果集合。
可选地，所述目标参考采样点包括第一参考采样点和/或第二参考采样点；
所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
在一个实施例中，填充模块1202，具体用于：若所述预测模式包括第二预测模式，则根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；根据所述第一参考采样点和所述填充后的参考采样点确定所述图像块 对应分区的预测结果集合。
可以理解的是，本实施例所描述的图像处理装置的各功能模块的功能可根据上述方法实施例中的方法具体实现，其具体实现过程可以参照上述方法实施例的相关描述，此处不再赘述。另外，对采用相同方法的有益效果描述，也不再进行赘述。
本申请实施例还提供一种智能终端，智能终端包括存储器、处理器，存储器上存储有图像处理程序，该图像处理程序被处理器执行时实现上述任一实施例中的图像处理方法的步骤。该智能终端可以是如图1所示的移动终端100。
在一可行的实施例中，如图1所示的移动终端100的处理器110可以用于调用存储器109中存储的图像处理程序，以执行如下操作：根据预设预测模式确定目标图像块中对应分区的预测结果集合，所述预测结果集合用于确定所述目标图像块的预测结果。
可选地，所述目标图像块包括第一分区和/或第二分区，所述第一分区和/或第二分区是由分割线划分得到的图像区域；所述预设预测模式包括所述目标图像块中分割线划分的分区所使用的预测模式；所述预测结果集合包括所述第一分区的第一预测结果集合和/或所述第二分区的第二预测结果集合。
在一个实施例中，处理器110还用于：根据第一划分模式集合确定目标图像块的目标划分模式参数；所述目标划分模式参数包括用于指示所述目标图像块中对应分区所使用的预测模式的预测模式指示信息。
在一个实施例中，处理器110，具体用于以下至少一项：若所述预测模式为第一预测模式，则根据所述目标图像块中对应分区的运动矢量确定所述目标图像块中对应分区的预测结果集合；若所述预测模式为第二预测模式，则根据所述目标图像块中对应分区的目标参考采样点确定所述目标图像块中对应分区的预测结果集合。
在一个实施例中，处理器110，具体用于：根据所述目标图像块的合并候选列表确定所述目标图像块中对应分区的第一运动矢量和/或第二运动矢量；根据所述第一运动矢量和/或所述第二运动矢量确定所述目标图像块中对应分区的预测结果集合。
可选地，所述目标参考采样点包括第一参考采样点和/或第二参考采样点。
可选地，包括以下至少一项：
所述第一参考采样点和所述第二参考采样点不同；
所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
可选地，所述第二预测模式包括至少一种类型的第二预测模式，若所述预测模式为第二预测模式，则处理器110，还用于：从所述至少一种类型的第二预测模式中确定所述目标图像块对应分区所使用的目标类型的第二预测模式，所述目标类型的第二预测模式用于确定所述目标图像块的预测结果集合。
在一个实施例中，处理器110，具体用于：确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；根据所述第一参考采样点和/或所述第二参考采样点确定所述目标图像块中对应分区的预测结果集合。
在一个实施例中，处理器110，具体用于：根据所述目标图像块中对应分区与所述目标图像块使用的分割线在所述目标图像块中的边界采样点之间的位置关系，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点。
在一个实施例中，处理器110，具体用于：根据边界映射表确定所述目标图像块使用的分割线所经过的边界；根据所述分割线所经过的边界确定所述边界采样点。
在一个实施例中，处理器110，具体用于以下至少一项：当所述目标图像块使用的分割线经过所述目标图像块的第一边界时，确定所述边界采样点包括第一边界采样点；当所述目标图像块使用的分割线经过所述目标图像块的第二边界时，确定所述边界采样点包括第二边界采样点。
可选地，处理器110具体还用于根据所述目标图像块的目标划分模式参数确定分割线方程；根据边界参考点和所述分割线方程确定至少一所述边界采样点。
在一个实施例中，处理器110，具体用于：根据所述至少一边界采样点确定第一坐标范围和/或第二坐标范围；根据所述第一坐标范围和/或所述第二坐标范围确定第一参考采样点和/或第二参考采样点。
在一个实施例中，处理器110，具体用于：根据所述目标图像块中对应分区的采样点与分割线之间的距离，确定所述目标图像块中对应分区的分区范围；基于第一采样点的坐标范围和所述分区范围确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；或者，基于与所述目标图像块相邻的第一采样点与所述分割线之间的距离信息，确定所述第一采样点为所述目标图像块中对应分区的第一参考采样点或者第二参考采样点。
在一个实施例中，处理器110，用于：根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；根据所述第一参考采样点和所述填充后的参考采样点确定所述目标图像块对应分区的预测结果集合。
在一个实施例中，处理器110，具体用于：在所述第一参考采样点中确定至少一个填充参考采样点；
基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值；基于所述填充值对所述第二参考采样点进行填充，得到填充后的参考采样点。
在一个实施例中，处理器110，具体用于：基于各个填充参考采样点与所述第二参考采样点之间的位置关系确定所述各个填充参考采样点的填充权重；基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值。
可选地，包括以下至少一项：
所述位置关系包括所述各个填充参考采样点和所述第二参考采样点之间的距离；
所述第一参考采样点包括与所述目标图像块的第一边界相邻的第一参考采样点，和/或与所述目标图像块的第二边界相邻的第一参考采样点；
所述至少一个填充参考采样点包括与所述第一边界相邻的第一参考采样点中的第一填充参考采样点，和/或与所述第二边界相邻的第一参考采样点中的第二填充参考采样点；
在一个实施例中，处理器110，具体用于：基于第一填充参考采样点的采样值和第一填充参考采样点的填充权重，和/或，第二填充参考采样点的采样值和第二填充参考采样点的填充权重，确定与对应边界相邻的所述第二参考采样点的填充值。
在一种可能的实施方式中，如图1所示的移动终端100的处理器110可以用于调用存储器109中存储的图像处理程序，以执行如下操作：用于通过预设策略确定目标参考采样点；根据所述目标参考采样点和预设预测模式，确定预测结果集合。
在一个实施例中，处理器110，具体用于：通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点。
在一个实施例中，处理器110，具体用于：从图像块分区的采样点中确定至少一边界采样点；根据参考采样点与所述至少一边界采样点之间的位置关系确定目标参考采样点。
可选地，所述至少一边界采样点包括第一边界采样点和/或第二边界采样点，处理器110，具体用于：根据所述第一边界采样点确定第一坐标范围，和/或，根据所述第二边界采样点确定第二坐标范围；
根据参考采样点与所述第一坐标范围之间的位置关系，和/或，根据所参考采样点与所述第二坐标范围之间的位置关系，确定目标参考采样点。
在一个实施例中，处理器110，具体用于：根据图像块分区中采样点确定分区范围；根据参考采样点和所述分区范围的位置关系确定目标参考采样点。
在一个实施例中，处理器110，具体用于：根据图像块的预测模式指示信息确定预设预测模式；根据所述预设预测模式和所述目标参考采样点，确定预测结果集合。
可选地，所述预设预测模式为以下至少一项：图像块中分割线划分的分区所使用的预测模式和/或相邻图像块所使用的预测模式。
在一个实施例中，处理器110，具体用于：若所述预测模式包括第一预测模式和/或第二预测模式，则根据运动矢量和/或目标参考采样点，确定图像块对应分区的预测结果集合；根据所述图像块对应分区的预测结果集合确定所述图像块的预测结果集合。
可选地，所述目标参考采样点包括第一参考采样点和/或第二参考采样点；
所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
在一个实施例中，处理器110，具体用于：若所述预测模式包括第二预测模式，则根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；根据所述第一参考采样点和所述填充后的参考采样点确定所述图像块对应分区的预测结果集合。
应当理解，本申请实施例中所描述的移动终端可执行上述任一实施例的方法描述，也可执行上述所对应实施例中对该图像处理装置的描述，在此不再赘述。另外，对采用相同方法的有益效果描述，也不再进行赘述。
本申请实施例还提供一种计算机可读存储介质，存储介质上存储有图像处理程序，图像处理程序被处理器执行时实现上述任一实施例中的图像处理方法的步骤。
在本申请提供的智能终端和计算机可读存储介质的实施例中，可以包含任一上述图像处理方法实施例的全部技术特征，说明书拓展和解释内容与上述方法的各实施例基本相同，在此不再做赘述。
图13是本申请实施例示出的一种控制器的硬件结构示意图。该控制器140包括：存储器1401和处理器1402，存储器1401用于存储程序指令，处理器1402用于调用存储器1401中的程序指令执行上述方法实施例中控制器所执行的步骤，其实现原理以及有益效果类似，此处不再进行赘述。
可选地，上述控制器还包括通信接口1403，该通信接口1403可以通过总线1404与处理器1402连接。处理器1402可以控制通信接口1403来实现控制器140的接收和发送的功能。
图14是本申请实施例示出的一种网络节点的硬件结构示意图。该网络节点150包括：存储器1501和处理器1502，存储器1501用于存储程序指令，处理器1502用于调用存储器1501中的程序指令执行上述方法实施例中首节点所执行的步骤，其实现原理以及有益效果类似，此处不再进行赘述。
可选地，上述网络节点还包括通信接口1503，该通信接口1503可以通过总线1504与处理器1502连接。处理器1502可以控制通信接口1503来实现网络节点150的接收和发送的功能。
本申请实施例还提供一种计算机程序产品，计算机程序产品包括计算机程序代码，当计算机程序代码在计算机上运行时，使得计算机执行如上各种可能的实施方式中的方法。
本申请实施例还提供一种芯片，包括存储器和处理器，存储器用于存储计算机程序，处理器用于从存储器中调用并运行计算机程序，使得安装有芯片的设备执行如上各种可能的实施方式中的方法。
可以理解，上述场景仅是作为示例，并不构成对于本申请实施例提供的技术方案的应用场景的限定，本申请的技术方案还可应用于其他场景。例如，本领域普通技术人员可知，随着系统架构的演变和新业务场景的出现，本申请实施例提供的技术方案对于类似的技术问题，同样适用。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
本申请实施例方法中的步骤可以根据实际需要进行顺序调整、合并和删减。
本申请实施例设备中的单元可以根据实际需要进行合并、划分和删减。
在本申请中，对于相同或相似的术语概念、技术方案和/或应用场景描述，一般只在第一次出现时进行详细描述，后面再重复出现时，为了简洁，一般未再重复阐述，在理解本申请技术方案等内容时，对于在后未详细描述的相同或相似的术语概念、技术方案和/或应用场景描述等，可以参考其之前的相关详细描述。
在本申请中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本申请技术方案的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本申请记载的范围。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，被控终端，或者网络设备等)执行本申请每个实施例的方法。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行计算机程序指令时，全部或部分地产生按照本申请实施例的流程或功能。计算机可以是通用计算机、专用计算机、计算机网络，或者其他可编程装置。计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线)或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。可用介质可以是磁性介质，(例如，软盘、存储盘、磁带)、光介质(例如，DVD)，或者半导体介质(例如固态存储盘Solid State Disk(SSD))等。
上述以软件功能模块的形式实现的集成的模块，可以存储在一个计算机可读取存储介质中。上述软件功能模块存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(英文：processor)执行本申请各个实施例方法的部分步骤。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (26)

  1. 一种图像处理方法，其中，包括：
    根据预设预测模式确定目标图像块中对应分区的预测结果集合，所述预测结果集合用于确定所述目标图像块的预测结果。
  2. 如权利要求1所述的方法，其中，包括以下至少一项：
    所述目标图像块包括第一分区和/或第二分区，所述第一分区和/或第二分区是由分割线划分得到的图像区域；
    所述预设预测模式包括所述目标图像块中分割线划分的分区所使用的预测模式；
    所述预测结果集合包括所述第一分区的第一预测结果集合和/或所述第二分区的第二预测结果集合。
  3. 如权利要求2所述的方法，其中，所述方法还包括：
    根据第一划分模式集合确定目标图像块的目标划分模式参数；所述目标划分模式参数包括用于指示所述目标图像块中对应分区所使用的预测模式的预测模式指示信息。
  4. 如权利要求2所述的方法，其中，所述根据预设预测模式确定目标图像块中对应分区的预测结果集合，包括以下至少一项：
    若所述预测模式为第一预测模式，则根据所述目标图像块中对应分区的运动矢量确定所述目标图像块中对应分区的预测结果集合；
    若所述预测模式为第二预测模式，则根据所述目标图像块中对应分区的目标参考采样点确定所述目标图像块中对应分区的预测结果集合。
  5. 如权利要求4所述的方法，其中，所述根据所述目标图像块中对应分区的运动矢量确定所述目标图像块中对应分区的预测结果集合，包括：
    根据所述目标图像块的合并候选列表和/或目标参考采样点确定所述目标图像块中对应分区的第一运动矢量和/或第二运动矢量；
    根据所述第一运动矢量和/或所述第二运动矢量确定所述目标图像块中对应分区的预测结果集合。
  6. 如权利要求4所述的方法，其中，所述方法还包括以下至少一项：
    所述目标参考采样点包括第一参考采样点和/或第二参考采样点；
    所述第二预测模式包括至少一种类型的第二预测模式。
  7. 如权利要求6所述的方法，其中，所述方法还包括以下至少一项：
    所述第一参考采样点和所述第二参考采样点不同；
    所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
    所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
    所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
    所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点；
    若所述预测模式为第二预测模式，则所述方法还包括：从所述至少一种类型的第二预测模式中确定所述目标图像块对应分区所使用的目标类型的第二预测模式，所述目标类型的第二预测模式用于确定所述目标图像块的预测结果集合。
  8. 如权利要求6所述的方法，其中，所述根据所述目标图像块中对应分区的目标参考采样点确定所述目标图像块中对应分区的预测结果集合，包括以下步骤：
    S21：确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；
    S22：根据所述第一参考采样点和/或所述第二参考采样点确定所述目标图像块中对应分区的预测结果集合。
  9. 如权利要求8所述的方法，其中，所述步骤S21包括以下至少一项：
    根据所述目标图像块中对应分区与所述目标图像块使用的分割线在所述目标图像块中的边界采样点之间的位置关系，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点；
    根据所述目标图像块中对应分区的采样点与分割线之间的距离，确定所述目标图像块中对应分区的分区范围，基于第一采样点的坐标范围和所述分区范围确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点，或者，基于与所述目标图像块相邻的第一采样点与所述分割线之间的距离信息，确定所述第一采样点为所述目标图像块中对应分区的第一参考采样点或者第二参考采样点。
  10. 如权利要求9所述的方法，其中，所述方法还包括：
    根据边界映射表确定所述目标图像块使用的分割线所经过的边界；
    根据所述分割线所经过的边界确定所述边界采样点。
  11. 如权利要求10所述的方法，其中，所述根据所述分割线所经过的边界确定所述边界采样点，包括以下至少一项：
    当所述目标图像块使用的分割线经过所述目标图像块的第一边界时，确定所述边界采样点包括第一边界采样点；
    当所述目标图像块使用的分割线经过所述目标图像块的第二边界时，确定所述边界采样点包括第二边界采样点。
  12. 如权利要求10所述的方法，其中，所述边界采样点的确定方式，包括：
    根据所述目标图像块的目标划分模式参数确定分割线方程；
    根据边界参考点和所述分割线方程确定至少一所述边界采样点。
  13. 如权利要求9至12中任一项所述的方法，其中，所述根据所述目标图像块中对应分区与所述目标图像块使用的分割线在所述目标图像块中的边界采样点之间的位置关系，确定所述目标图像块中对应分区的第一参考采样点和/或第二参考采样点，包括：
    根据所述至少一边界采样点确定第一坐标范围和/或第二坐标范围；
    根据所述第一坐标范围和/或所述第二坐标范围确定第一参考采样点和/或第二参考采样点。
  14. 如权利要求8至12中任一项所述的方法，其中，所述步骤S22包括：
    根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；
    根据所述第一参考采样点和所述填充后的参考采样点确定所述目标图像块对应分区的预测结果集合。
  15. 如权利要求14所述的方法，其中，所述根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点，包括：
    在所述第一参考采样点中确定至少一个填充参考采样点；
    基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值；
    基于所述填充值对所述第二参考采样点进行填充，得到填充后的参考采样点。
  16. 如权利要求15所述的方法，其中，所述基于所述至少一个填充参考采样点的采样值确定所述第二参考采样点的填充值，包括：
    基于各个填充参考采样点与所述第二参考采样点之间的位置关系确定所述各个填充参考采样点的填充权重；
    基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值。
  17. 如权利要求16所述的方法，其中，包括以下至少一项：
    所述位置关系包括所述各个填充参考采样点和所述第二参考采样点之间的距离；
    所述第一参考采样点包括与所述目标图像块的第一边界相邻的第一参考采样点，和/或与所述目标图像块的第二边界相邻的第一参考采样点；
    所述至少一个填充参考采样点包括与所述第一边界相邻的第一参考采样点中的第一填充参考采样点，和/或与所述第二边界相邻的第一参考采样点中的第二填充参考采样点；
    所述基于所述填充权重和所述各个填充参考采样点的采样值确定所述第二参考采样点的填充值，包括：
    基于第一填充参考采样点的采样值和第一填充参考采样点的填充权重，和/或，第二填充参考采样点的采样值和第二填充参考采样点的填充权重，确定与对应边界相邻的所述第二参考采样点的填充值。
  18. 一种图像处理方法，其中，包括以下步骤：
    S1：通过预设策略确定目标参考采样点；
    S2：根据所述目标参考采样点和预设预测模式，确定预测结果集合。
  19. 如权利要求18所述的方法，其中，所述S1步骤，包括：
    通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点。
  20. 如权利要求19所述的方法，其中，所述通过参考采样点与图像块分区中采样点的位置关系确定目标参考采样点，包括以下至少一项：
    从图像块分区的采样点中确定至少一边界采样点，根据参考采样点与所述至少一边界采样点之间的位置关系确定目标参考采样点；
    根据图像块分区中采样点确定分区范围，根据参考采样点和所述分区范围的位置关系确定目标参考采样点。
  21. 如权利要求20所述的方法，其中，所述至少一边界采样点包括第一边界采样点和/或第二边界采样点，所述根据参考采样点与所述至少一边界采样点之间的位置关系确定目标参考采样点，包括：
    根据所述第一边界采样点确定第一坐标范围，和/或，根据所述第二边界采样点确定第二坐标范围；
    根据参考采样点与所述第一坐标范围之间的位置关系，和/或，根据所参考采样点与所述第二坐标范围之间的位置关系，确定目标参考采样点。
  22. 如权利要求18至21中任一项所述的方法，其中，所述步骤S2包括：
    根据图像块的预测模式指示信息确定预设预测模式；
    根据所述预设预测模式和所述目标参考采样点，确定预测结果集合。
  23. 如权利要求22所述的方法，其中，包括以下至少一项：
    所述预设预测模式为：图像块中分割线划分的分区所使用的预测模式和/或相邻图像块所使用的预测模式；
    所述预设预测模式为图像块中分割线划分的分区所使用的预测模式，所述根据所述预设预测模式和所述目标参考采样点，确定预测结果集合，包括：若所述预测模式包括第一预测模式和/或第二预测模式，则根据运动矢量和/或目标参考采样 点，确定图像块对应分区的预测结果集合，根据所述图像块对应分区的预测结果集合确定所述图像块的预测结果集合；
    所述目标参考采样点包括第一参考采样点和/或第二参考采样点；
    所述第一参考采样点与所述对应分区相邻且与另一分区不相邻；
    所述第二参考采样点与所述对应分区不相邻且与所述另一分区相邻；
    所述第一参考采样点相对于所述对应分区的位置关系与所述第二参考采样点相对于所述对应分区的位置关系不同；
    所述第一参考采样点或者所述第二参考采样点为与所述目标图像块所在的编码树单元相邻，且与所述目标图像块不相邻的像素点。
  24. 如权利要求23所述的方法，其中，若所述预测模式包括第二预测模式，则根据所述目标参考采样点确定图像块对应分区的预测结果集合，包括：
    若所述预测模式包括第二预测模式，则根据所述第一参考采样点对所述第二参考采样点进行填充，得到填充后的参考采样点；
    根据所述第一参考采样点和所述填充后的参考采样点确定所述图像块对应分区的预测结果集合。
  25. 一种智能终端，其中，所述智能终端包括：存储器、处理器，其中，所述存储器上存储有图像处理程序，所述图像处理程序被所述处理器执行时实现如权利要求1所述的图像处理方法的步骤。
  26. 一种计算机可读存储介质，其中，所述存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1所述的图像处理方法的步骤。
PCT/CN2023/090367 2022-05-07 2023-04-24 图像处理方法、智能终端及存储介质 Ceased WO2023216866A1 (zh)

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