Local illumination compensation with extended model

Abstract

Systems, devices, and methods for local illumination compensation (LIC) with an extended model are described herein. An example device for video decoding may determine an LIC model of a block; obtaining a set of LIC parameters based on the determined LIC model of the block; and decoding the block based on the set of LIC parameters. An example device for video encoding may select an LIC model for a block from a plurality of LIC models; obtaining a set of LIC parameters based on an LIC model selected for the block; and encoding the block based on the set of LIC parameters.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of European Provisional Patent Application No. EP22307008.7 filed on December 22, 2022, the contents of which are hereby incorporated herein by reference. Background Art

[0003] Video coding systems can be used to compress digital video signals, for example, to reduce the storage and / or transmission bandwidth required for such signals.Video coding systems can include, for example, block-based, wavelet-based, and / or object-based systems. Summary of the Invention

[0004] Systems, devices, and methods for local illumination compensation (LIC) with extended models are described herein. For example, filter-based models, gradient-based models, convolutional cross-component models (CCCMs), and / or the like can be used for LIC.

[0005] An example device for video decoding (e.g., a video decoder) may determine to use a polynomial local illumination compensation (LIC) model for a block. The device may obtain a set of LIC parameters associated with the polynomial LIC model for the block. The device may decode the block based on the set of LIC parameters. The device may receive a LIC model indication in video data. The device may determine to use the polynomial LIC model for the block based on the LIC model indication.

[0006] An example video encoding device (e.g., a video encoder) may determine to use a polynomial LIC model for a block. The device may obtain a set of LIC parameters associated with the polynomial LIC model for the block. The device may encode the block based on the set of LIC parameters. The device may send a LIC model indication in the video data, wherein the LIC model indication indicates to use the polynomial LIC model for the block.

[0007] The polynomial LIC model may be a filter-based model. The device (e.g., a video decoder or a video encoder) may identify template samples for the block. The device may identify a first reference template sample corresponding to the template sample for the block in a template of a reference block for the block. The device may identify a plurality of second reference template samples adjacent to the first reference template sample. The device may derive a set of filter coefficients based on minimizing differences between the template sample for the block and corresponding predicted template samples obtained based on the plurality of second reference template samples.

[0008] The polynomial LIC model may be a filter-based model. For a sample position in the block, the device may identify multiple reference samples of a reference block associated with the block. The multiple reference samples may include a center reference sample corresponding to the sample position. The device may apply corresponding LIC parameters from the set of LIC parameters to the multiple reference samples to generate predicted samples for the block. The LIC parameters corresponding to the center reference sample may be applied to the square of the center reference sample.

[0009] The polynomial LIC model may be a gradient-based model. The device may obtain the set of LIC parameters by deriving the set of LIC parameters based on reference samples in a template of the block and a gradient of the reference samples. The device may obtain the set of LIC parameters by deriving the set of LIC parameters based on at least one of: a first reference block and a second reference block associated with the block; or a temporal gradient associated with the block.

[0010] The polynomial LIC model may be a gradient-based model. The device may apply the set of LIC parameters to reference samples of a reference block and gradients of the reference samples to obtain a refined prediction of the block. The device may reconstruct the block based on the refined prediction of the block.

[0011] In some examples, the device may determine to use a linear LIC model for the block, obtain a set of LIC parameters associated with the linear LIC model for the block, and decode / encode the block based on a second set of LIC parameters.

[0012] The linear LIC model may be a filter-based model. The device may identify template samples for the block. The device may identify a first reference template sample corresponding to the template sample for the block in a template of a reference block for the block. The device may identify a plurality of second reference template samples adjacent to the first reference template sample. The device may derive a set of filter coefficients based on minimizing differences between the template sample for the block and corresponding predicted template samples obtained based on the plurality of second reference template samples.

[0013] The linear LIC model may be a filter-based model including a set of filter coefficients. For a sample position in the block, the device may identify a plurality of reference samples of a reference block associated with the block. The device may apply corresponding filter coefficients from the set of filter coefficients to a plurality of adjacent samples to generate a predicted sample for the block.

[0014] The linear LIC model may be a gradient-based model. The device may obtain the set of LIC parameters by deriving the set of LIC parameters based on a gradient of a reference sample of a reference block associated with the block.

[0015] An example device for video decoding may determine a LIC model for a block. The device may obtain a LIC parameter set based on the determined LIC model for the block. The device may decode the block based on the LIC parameter set. In an example, a LIC model indication may be received in video data, and the LIC model may be determined based on the LIC model indication. The device may refine a prediction for the block based on the obtained LIC parameter set, and may reconstruct the block based on the refined prediction for the block.

[0016] A device for video encoding may select a LIC model for a block from a plurality of LIC models. The device may obtain a LIC parameter set based on the selected LIC model for the block. The device may encode the block based on the LIC parameter set. The device may include an indication of the selected LIC model in the video data. The device may refine a prediction for the block based on the obtained LIC parameter set, and determine a residual for the block based on the refined prediction for the block.

[0017] Based on the determined (e.g., selected) LIC model for the block being a filter-based model, LIC parameters may be derived based on the filter-based model. Based on the determined (e.g., selected) LIC model for the block being a gradient-based model, LIC parameters may be derived based on the gradient-based model. Based on the determined (e.g., selected) LIC model for the block being a CCCM, LIC parameters may be derived based on a polynomial model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A is a system diagram illustrating an example communication system in which one or more disclosed embodiments may be implemented.

[0019] Figure 1B is a diagram illustrating that according to an embodiment, Figure 1A A system diagram of an example wireless transmit / receive unit (WTRU) for use within a communication system is shown in FIG.

[0020] Figure 1C is a diagram illustrating that according to an embodiment, Figure 1A A system diagram of an example radio access network (RAN) and an example core network (CN) used within the communication system illustrated in FIG.

[0021] Figure 1D is a diagram illustrating that according to an embodiment, Figure 1A System diagram of a further example RAN and a further example CN for use within the communication system illustrated in FIG.

[0022] Figure 2 is a diagram illustrating an example block-based video encoder.

[0023] Figure 3 is a diagram illustrating an example video decoder.

[0024] Figure 4 is a diagram illustrating an example of a system in which various aspects and examples may be implemented.

[0025] Figure 5 An example LIC parameter estimation process is illustrated.

[0026] Figure 6 An example LIC parameter derivation process based on template samples derived on a sub-block basis is illustrated.

[0027] Figure 7 Illustrated are example locations of samples used in the Cross Component Linear Model (CCLM) mode.

[0028] Figure 8 An example of a linear relationship solved by linear regression is shown.

[0029] Figure 9A An example spatial portion of a convolution filter is shown.

[0030] Figure 9B An example reference region is illustrated with six rows of chroma samples above and to the left of the block.

[0031] Figure 10 Illustrated are example locations of samples that may be used to derive multiple linear regression (MLR) parameters for a filter-based model.

[0032] Figure 11 Four example Sobel-based gradient patterns are illustrated from which the gradient G(x,y) may be computed.

[0033] Figure 12 Illustrated is an example video game image in which light gradually propagates across the image.

[0034] Figure 13 An example of using LIC decoding with a simple linear model is illustrated.

[0035] Figure 14 An example filter coefficient derivation process for LIC with a filter-based model is illustrated.

[0036] Figure 15 Example filter shapes for a LIC with a filter-based model are illustrated.

[0037] Figure 16 Illustrated are example spatial portions of convolutional filters of a LIC with a gradient-based model.

[0038] Figure 17An example is illustrated illustrating 16 example Sobel-based gradient patterns from which the spatial gradient of the LIC with the gradient-based model can be calculated.

[0039] Figure 18 An example of decoding a block using LIC with an extended model is illustrated. DETAILED DESCRIPTION

[0040] A detailed description of the illustrative embodiments will now be described with reference to the various drawings. While this description provides detailed examples of possible implementations, it should be noted that the details are intended to be exemplary and in no way limit the scope of the application.

[0041] Figure 1A FIG1 is a diagram illustrating an example communication system 100 in which one or more disclosed embodiments may be implemented. The communication system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communication system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communication system 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single carrier FDMA (SC-FDMA), zero tail unique word DFT spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtered OFDM, filter bank multi-carrier (FBMC), and the like.

[0042] like Figure 1AAs shown in FIG, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RAN 104 / 113, CN 106 / 115, public switched telephone network (PSTN) 108, the Internet 110, and other networks 112. However, it will be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d (any of which may be referred to as a "station" and / or "STA") may be configured to transmit and / or receive wireless signals and may include user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular phone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watch or other wearable device, a head-mounted display (HMD), a vehicle, a drone, medical equipment and applications (e.g., remote surgery), industrial equipment and applications (e.g., robots and / or other wireless devices operating in the context of an industrial and / or automated process chain), a consumer electronic device, a device operating on a commercial and / or industrial wireless network, and the like. Any of the WTRUs 102a, 102b, 102c, and 102d may be interchangeably referred to as a UE.

[0043] The communication system 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, an NRNodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0044] Base station 114a may be part of RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. Base station 114a and / or base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as cells (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide wireless service coverage to a specific geographic area, which may be relatively fixed or may change over time. A cell may be further divided into cell sectors. For example, the cell associated with base station 114a may be divided into three sectors. Thus, in one embodiment, base station 114a may include three transceivers, one for each sector of the cell. In an embodiment, base station 114a may employ multiple-input multiple-output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and / or receive signals in a desired spatial direction.

[0045] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0046] More specifically, as noted above, the communication system 100 may be a multiple access system and may employ one or more channel access schemes such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may utilize Wideband CDMA (WCDMA) to establish the air interface 115 / 116 / 117. WCDMA may include communication protocols such as High Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High Speed ​​Downlink (DL) Packet Access (HSDPA) and / or High Speed ​​UL Packet Access (HSUPA).

[0047] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-APro).

[0048] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology, such as NR radio access, that may establish the air interface 116 using New Radio (NR).

[0049] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement both LTE radio access and NR radio access, for example, using dual connectivity (DC) principles. Thus, the air interface utilized by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., eNBs and gNBs).

[0050] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi)), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0051] Figure 1AThe base station 114b in the may be, for example, a wireless router, a Home Node B, a Home eNode B, or an access point, and may utilize any suitable RAT to facilitate wireless connectivity in a local area, such as a business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a road, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.) to establish a picocell or a femtocell. Figure 1A As shown in FIG, base station 114b may have a direct connection to the Internet 110. Thus, base station 114b may not need to access the Internet 110 via CN 106 / 115.

[0052] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications, and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, delay requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform advanced security functions, such as user authentication. Although not described in Figure 1A Although not shown in the figures, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may utilize NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) that employs GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0053] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network that provides plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and / or the Internet Protocol (IP) from the TCP / IP internet protocol suite. The networks 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0054] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communication system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks via different wireless links). Figure 1A The WTRU 102c shown in FIG. 1 may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0055] Figure 1B is a system diagram illustrating an example WTRU 102. Figure 1B As shown in FIG, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power supply 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138. It will be appreciated that the WTRU 102 may include any subcombination of the foregoing elements while remaining consistent with an embodiment.

[0056] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. Although Figure 1B The processor 118 and the transceiver 120 are depicted as separate components, but it will be understood that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0057] The transmit / receive element 122 can be configured to transmit signals to a base station (e.g., base station 114a) or receive signals from a base station (e.g., base station 114a) via the air interface 116. For example, in one embodiment, the transmit / receive element 122 can be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 can be an emitter / detector configured to transmit and / or receive, for example, IR, UV, or visible light signals. In another embodiment, the transmit / receive element 122 can be configured to transmit and / or receive both RF signals and optical signals. It will be understood that the transmit / receive element 122 can be configured to transmit and / or receive any combination of wireless signals.

[0058] Although the transmit / receive element 122 Figure 1B Although depicted as a single element in FIG. 1 , the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0059] The transceiver 120 may be configured to modulate signals to be transmitted by the transmit / receive element 122 and demodulate signals received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers to, for example, enable the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11.

[0060] The processor 118 of the WTRU 102 may be coupled to a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light emitting diode (OLED) display unit) and may receive user input data from the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. Furthermore, the processor 118 may access information from and store data in any type of suitable memory, such as non-removable memory 130 and / or removable memory 132. The non-removable memory 130 may include random access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0061] The processor 118 may receive power from the power source 134 and may be configured to distribute and / or control power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0062] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to or in lieu of the information from the GPS chipset 136, the WTRU 102 may receive location information from a base station (e.g., base stations 114a, 114b) over the air interface 116 and / or determine its location based on the timing of signals received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by any suitable location-determination method while remaining consistent with an embodiment.

[0063] The processor 118 may be further coupled to other peripherals 138, which may include one or more software modules and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, Modules, frequency modulation (FM) radio units, digital music players, media players, video game player modules, internet browsers, virtual reality and / or augmented reality (VR / AR) devices, activity trackers, and the like. Peripheral devices 138 may include one or more sensors, which may be one or more of the following: a gyroscope, an accelerometer, a Hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0064] The WTRU 102 may include a full-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for both UL (e.g., for transmission) and downlink (e.g., for reception)) may be concurrent and / or simultaneous. The full-duplex radio may include an interference management unit for reducing and / or substantially eliminating self-interference via hardware (e.g., a choke) or via signal processing performed by a processor (e.g., a separate processor (not shown) or via the processor 118). In an embodiment, the WTRU 102 may include a half-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for both UL (e.g., for transmission) or downlink (e.g., for reception)) may be concurrent and / or simultaneous.

[0065] Figure 1C 1 is a system diagram illustrating the RAN 104 and the CN 106 in accordance with an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0066] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the eNode-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the eNode-B 160a, for example, may use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a.

[0067] Each of the eNode-Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, and the like. Figure 1C As shown in FIG, eNode-Bs 160a, 160b, 160c may communicate with each other via an X2 interface.

[0068] Figure 1C The CN 106 shown in FIG may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements is depicted as part of the CN 106, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0069] The MME 162 may be connected to each of the eNode-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, and the like. The MME 162 may also provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0070] The SGW 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to and from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions such as anchoring the user plane during inter-eNode B handovers, triggering paging when downlink data is available for the WTRUs 102a, 102b, 102c, managing and storing the context of the WTRUs 102a, 102b, 102c, and the like.

[0071] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0072] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may be in communication with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that serves as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers.

[0073] Even though the WTRU Figures 1A-1D Although described as a wireless terminal, it is contemplated that in certain representative embodiments, such a terminal may (eg, temporarily or permanently) employ a wired communication interface with a communication network.

[0074] In a representative embodiment, the other network 112 may be a WLAN.

[0075] A WLAN in infrastructure basic service set (BSS) mode may have an access point (AP) for the BSS and one or more stations (STAs) associated with the AP. The AP may have access to or an interface with a distribution system (DS) or another type of wired / wireless network that carries traffic into and / or out of the BSS. Traffic originating from outside the BSS and destined for a STA may reach the AP and be delivered to the STA. Traffic originating from a STA destined for a destination outside the BSS may be sent to the AP for delivery to the destination. Traffic between STAs within a BSS may be sent through the AP, for example, where a source STA may send traffic to the AP, and the AP may deliver the traffic to the destination STA. Traffic between STAs within a BSS may be considered and / or referred to as point-to-point traffic. Point-to-point traffic may be sent between a source and destination STA (e.g., directly between them) using direct link setup (DLS). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunneled DLS (TDLS). A WLAN using an independent BSS (IBSS) mode may not have an AP, and STAs (eg, all STAs) within or using the IBSS may communicate directly with each other. The IBSS communication mode may sometimes be referred to herein as an "ad-hoc" communication mode.

[0076] When using 802.11ac infrastructure operation mode or a similar operation mode, the AP can transmit beacons on a fixed channel (such as a primary channel). The primary channel can be a fixed width (e.g., a 20 MHz wide bandwidth) or a width dynamically set via signaling. The primary channel can be the operating channel of the BSS and can be used by STAs to establish a connection with the AP. In certain representative embodiments, for example, carrier sense multiple access with collision avoidance (CSMA / CA) can be implemented in an 802.11 system. For CSMA / CA, STAs (e.g., each STA) including the AP can sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a specific STA, the specific STA can back off. One STA (e.g., only one station) can transmit at any given time in a given BSS.

[0077] High throughput (HT) STAs may communicate using a 40 MHz wide channel, for example, via a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels to form a 40 MHz wide channel.

[0078] Very high throughput (VHT) STAs can support 20MHz, 40MHz, 80MHz, and / or 160MHz wide channels. 40MHz and / or 80MHz channels can be formed by combining consecutive 20MHz channels. A 160MHz channel can be formed by combining 8 consecutive 20MHz channels, or by combining two non-contiguous 80MHz channels (which can be referred to as an 80+80 configuration). For the 80+80 configuration, after channel coding, the data can pass through a segment parser that can separate the data into two streams. Each stream can be separably subjected to inverse fast Fourier transform (IFFT) processing and time domain processing. The streams can be mapped onto two 80MHz channels, and the data can be transmitted by the transmitting STA. At the receiver of the receiving STA, the above operations for the 80+80 configuration can be reversed, and the combined data can be sent to the media access control (MAC).

[0079] 802.11af and 802.11ah support operating modes below 1 GHz. The channel operating bandwidth and carrier are reduced in 802.11af and 802.11ah relative to those used in 802.11n and 802.11ac. 802.11af supports 5 MHz, 10 MHz, and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah can support meter type control / machine type communication, such as MTC devices in macro coverage areas. MTC devices may have certain capabilities, such as limited capabilities, including support for (e.g., only support for) certain bandwidths and / or limited bandwidths. MTC devices may include batteries with battery life above a threshold (e.g., for maintaining very long battery life).

[0080] WLAN systems that can support multiple channels and channel bandwidths such as 802.11n, 802.11ac, 802.11af, and 802.11ah include channels that can be designated as primary channels. The primary channel can have a bandwidth equal to the maximum common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel can be set and / or limited by a STA (that supports the minimum bandwidth operating mode) from among all STAs operating in the BSS. In the example of 802.11ah, for a STA (e.g., an MTC-type device) that supports (e.g., only supports) 1 MHz mode, the primary channel can be 1 MHz wide, even if the AP and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or network allocation vector (NAV) settings can depend on the status of the primary channel. If the primary channel is busy, for example, because a STA (that only supports 1 MHz operating mode) is transmitting to the AP, the entire available frequency band can be considered busy even if most of the frequency band remains idle and may be available.

[0081] In the United States, the available frequency band for 802.11ah is from 902 MHz to 928 MHz. In South Korea, the available frequency band is from 917.5 MHz to 923.5 MHz. In Japan, the available frequency band is from 916.5 MHz to 927.5 MHz. Depending on the country code, the total bandwidth available for 802.11ah ranges from 6 MHz to 26 MHz.

[0082] Figure 1D 1 is a system diagram illustrating the RAN 113 and the CN 115 according to an embodiment. As noted above, the RAN 113 may employ NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0083] The RAN 113 may include gNBs 180a, 180b, and 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, and 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, and 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, and 180c may implement MIMO technology. For example, the gNBs 180a and 180b may utilize beamforming to transmit signals to and / or receive signals from the gNBs 180a, 180b, and 180c. Thus, the gNB 180a may, for example, use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a. In an embodiment, the gNBs 180a, 180b, and 180c may implement carrier aggregation techniques. For example, the gNB 180a may transmit multiple component carriers (not shown) to the WTRU 102a. A subset of these component carriers may be on unlicensed spectrum, while the remaining component carriers may be on licensed spectrum. In an embodiment, the gNBs 180a, 180b, and 180c may implement coordinated multi-point (CoMP) techniques. For example, the WTRU 102a may receive coordinated transmissions from gNB 180a and gNB 180b (and / or gNB 180c).

[0084] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with scalable numerologies. For example, OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or transmission time intervals (TTIs) of varying or scalable lengths (e.g., containing varying numbers of OFDM symbols and / or varying absolute time lengths).

[0085] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c while not accessing other RANs (e.g., such as the eNode-Bs 160a, 160b, 160c). In a standalone configuration, the WTRUs 102a, 102b, 102c may use one or more of the gNBs 180a, 180b, 180c as mobility anchor points. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate / connect to the gNBs 180a, 180b, 180c while also communicating / connecting to another RAN, such as the eNode-Bs 160a, 160b, 160c. For example, the WTRUs 102a, 102b, 102c may implement DC principles to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In a non-standalone configuration, the eNode-Bs 160a, 160b, 160c may serve as mobility anchors for the WTRUs 102a, 102b, 102c and the gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0086] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in UL and / or DL, support of network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards a user plane function (UPF) 184a, 184b, routing of control plane information towards an access and mobility management function (AMF) 182a, 182b, and the like. Figure 1D As shown in , gNBs 180a, 180b, and 180c can communicate with each other via the Xn interface.

[0087] Figure 1DThe CN 115 shown in FIG may include at least one AMF 182 a, 182 b, at least one UPF 184 a, 184 b, at least one session management function (SMF) 183 a, 183 b, and possibly data networks (DNs) 185 a, 185 b. While each of the aforementioned elements is depicted as part of the CN 115, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0088] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N2 interface and may serve as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, supporting network slicing (e.g., handling of different PDU sessions with different requirements), selecting a specific SMF 183a, 183b, managing registration areas, terminating NAS signaling, mobility management, and the like. Network slicing may be used by the AMF 182a, 182b to customize CN support for the WTRUs 102a, 102b, 102c based on the type of services utilized by the WTRUs 102a, 102b, 102c. For example, different network slices may be established for different use cases, such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced massive mobile broadband (eMBB) access, services for machine type communication (MTC) access, and / or the like. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) employing other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies, such as WiFi.

[0089] The SMF 183a, 183b may connect to the AMF 182a, 182b in the CN 115 via the N11 interface. The SMF 183a, 183b may also connect to the UPF 184a, 184b in the CN 115 via the N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b and configure the routing of traffic through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notification, and the like. The PDU session type may be IP-based, non-IP-based, Ethernet-based, and the like.

[0090] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, and the like.

[0091] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include, or may communicate with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that serves as an interface between the CN 115 and the PSTN 108. Additionally, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may connect to a local data network (DN) 185a, 185b through the UPFs 184a, 184b via the N3 interface to the UPFs 184a, 184b and the N6 interface between the UPFs 184a, 184b and the DNs 185a, 185b.

[0092] Given that Figures 1A-1D as well as Figures 1A-1D

[0015] As described herein, one or more or all of the functions described herein with reference to one or more of the following may be performed by one or more emulated devices (not shown): the WTRUs 102a-d, base stations 114a-b, eNode-Bs 160a-c, MMEs 162, SGWs 164, PGWs 166, gNBs 180a-c, AMFs 182a-b, UPFs 184a-b, SMFs 183a-b, DNs 185a-b, and / or any other device(s) described herein. An emulated device may be one or more devices configured to emulate one or more or all of the functions described herein. For example, an emulated device may be used to test other devices and / or simulate network and / or WTRU functions.

[0093] The emulation device can be designed to implement one or more tests of other devices in a laboratory environment and / or in an operator network environment. For example, one or more emulation devices can perform one or more or all functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. One or more emulation devices can perform one or more functions or all functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device can be directly coupled to another device for testing and / or can use over-the-air wireless communication to perform testing purposes.

[0094] One or more simulation devices can perform one or more (including all) functions without being implemented / deployed as part of a wired and / or wireless communication network. For example, the simulation device can be used in a test scenario in a test lab and / or a non-deployed (e.g., testing) wired and / or wireless communication network to implement testing of one or more components. One or more simulation devices can be test equipment. Direct RF coupling and / or wireless communication via RF circuitry (e.g., which can include one or more antennas) can be used by the simulation device to transmit and / or receive data.

[0095] This application describes various aspects, including tools, features, examples or embodiments, models, methods, etc. Many of these aspects are described in detail, and at least in order to illustrate the various characteristics, they are generally described in a manner that may sound restrictive. However, this is for the purpose of describing clearly and does not limit the application or scope of those aspects. In fact, all different aspects can be combined and interchanged to provide further aspects. In addition, the aspects can also be combined and interchanged with various aspects described in earlier submissions.

[0096] The various aspects described and contemplated in this application may be implemented in many different forms. Figure 5-18 Some embodiments may be provided, but other embodiments are contemplated. Figure 5-18 The discussion does not limit the breadth of implementation. At least one of the aspects generally relates to video encoding and decoding, and at least one other aspect generally relates to transmitting a generated or encoded bitstream. These and other aspects can be implemented as methods, apparatus, a computer-readable storage medium having stored thereon instructions for encoding or decoding video data according to any of the methods described, and / or a computer-readable storage medium having stored thereon a bitstream generated according to any of the methods described.

[0097] In this application, the terms “reconstruction” and “decoding” may be used interchangeably, the terms “pixel” and “sample” may be used interchangeably, and the terms “image”, “picture” and “frame” may be used interchangeably.

[0098] The terms HDR (high dynamic range) and SDR (standard dynamic range) may be used in this disclosure. Those terms generally convey a specific value of dynamic range to one of ordinary skill in the art. However, additional embodiments are also contemplated in which references to HDR are understood to mean "higher dynamic range" and references to SDR are understood to mean "lower dynamic range." Such additional embodiments are not constrained by any specific dynamic range value that may often be associated with the terms "high dynamic range" and "standard dynamic range."

[0099] Various methods are described herein, and each method in the method includes one or more steps or actions for realizing the method. Unless the correct operation method requires the steps or actions of a specific order, the order and / or use of specific steps and / or actions can be modified or combined. In addition, in various embodiments, terms such as "first", "second" etc. can be used to modify elements, components, steps, operations etc., such as, for example, "first decoding" and "second decoding". Unless otherwise required, using such terms does not mean the sequencing of the operation to modification. Therefore, in this example, the first decoding does not need to be performed before the second decoding, and can occur in, for example, before, during, or in the time period overlapping with the second decoding.

[0100] The various methods and other aspects described in this application can be used to modify modules, e.g. Figure 2 and Figure 3 , and intra prediction and entropy encoding and / or decoding modules (260, 360, 245, 330) of the video encoder 200 and decoder 300, respectively, are shown in FIG. Furthermore, the subject matter disclosed herein presents aspects that are not limited to VVC or HEVC and can be applied, for example, to any type, format, or version of video coding (whether described in a standard or recommendation, whether pre-existing or developed in the future), as well as extensions of any such standards and recommendations (e.g., including VVC and HEVC). Unless otherwise indicated or technically excluded, the various aspects described in this application can be used alone or in combination.

[0101] Various numerical values ​​are used in the examples described herein, such as coefficients, block sizes, etc. These and other specific values ​​are used for purposes of describing the examples, and the described aspects are not limited to these specific values.

[0102] Figure 2 is a diagram illustrating an example video encoder (eg, an example block-based hybrid video encoder) 200. Variations of the example encoder 200 are contemplated, but for clarity, the encoder 200 is described below without describing all contemplated variations.

[0103] Before being encoded, the video sequence may undergo a pre-encoding process (201), for example, applying a color transform to the input color picture (e.g., converting from RGB 4:4:4 to YCbCr 4:2:0), or performing a remapping of the input picture components to obtain a more resilient to compression signal distribution (e.g., using histogram equalization of one of the color components). Metadata may be associated with the pre-processing and attached to the bitstream.

[0104] In encoder 200, a picture is encoded by encoder elements as described below. The picture to be encoded is segmented (202) and processed in units of, for example, coding units (CUs). For example, each unit is encoded using intra or inter mode. When a unit is encoded in intra mode, it performs intra prediction (260). In inter mode, motion estimation (275) and compensation (270) are performed. The encoder decides (205) whether to use intra mode or inter mode to encode the unit and indicates the intra / inter decision by, for example, a prediction mode flag. For example, a prediction residual is calculated by subtracting (210) the predicted block from the original image block.

[0105] The prediction residual is then transformed (225) and quantized (230). The quantized transform coefficients, along with motion vectors and other syntax elements, are entropy encoded (245) to output a bitstream. The encoder can skip the transform and apply quantization directly to the untransformed residual signal. The encoder can bypass both the transform and quantization, i.e., encode the residual directly without applying the transform or quantization process.

[0106] The encoder decodes the coded block to provide a reference for further prediction. The quantized transform coefficients are dequantized (240) and inverse transformed (250) to decode the prediction residual. The image block is reconstructed by combining (255) the decoded prediction residual and the prediction block. A loop filter (265) is applied to the reconstructed picture to perform, for example, deblocking / SAO (sample adaptive offset) filtering to reduce coding artifacts. The filtered image is stored in a reference picture buffer (280).

[0107] Figure 3 is a diagram illustrating an example of a video decoder 300. In the example decoder 300, a bitstream is decoded by decoder elements as described below. The video decoder 300 generally performs the same operations as described above. Figure 2 The decoding process is the reverse of the encoding process described in . The encoder 200 also typically performs video decoding as part of encoding the video data.

[0108] In particular, the input to the decoder comprises a video bitstream, which may be generated by the video encoder 200. The bitstream is first entropy decoded (330) to obtain transform coefficients, motion vectors, and other encoding information. Picture segmentation information indicates how the picture is segmented. Thus, the decoder can divide (335) the picture according to the decoded picture segmentation information. The transform coefficients are dequantized (340) and inverse transformed (350) to decode the prediction residual. The image block is reconstructed by combining (355) the decoded prediction residual and the prediction block. The prediction block may be obtained (370) from intra-frame prediction (360) or motion compensated prediction (i.e., inter-frame prediction) (375). A loop filter (365) is applied to the reconstructed image. The filtered image is stored at a reference picture buffer (380). For a given picture, the contents of the reference picture buffer 380 on the decoder 300 side may be the same as the contents of the reference picture buffer 280 on the encoder 200 side for the same picture.

[0109] The decoded picture may further undergo post-decoding processing (385), such as an inverse color transform (e.g., from YCbCr 4:2:0 to RGB 4:4:4) or inverse remapping that performs the inverse of the remapping process performed in the pre-encoding process (201). The post-decoding processing may use metadata derived in the pre-encoding process and signaled in the bitstream.

[0110] Figure 4 4 is a diagram showing an example of a system in which the various aspects and embodiments described herein can be implemented. System 400 can be implemented as a device including the various components described below, and is configured to perform one or more aspects of the various aspects described in this document. Examples of such devices include, but are not limited to, various electronic devices, such as personal computers, laptop computers, smart phones, tablet computers, digital multimedia set-top boxes, digital television receivers, personal video recording systems, connected home appliances and servers. The elements of system 400 can be implemented individually or in combination in a single integrated circuit (IC), multiple ICs and / or discrete components. For example, in at least one example, the processing and encoder / decoder elements of system 400 are distributed across multiple ICs and / or discrete components. In various embodiments, system 400 is communicatively coupled to one or more other systems or other electronic devices via, for example, a communication bus or by dedicated input and / or output ports. In various embodiments, system 400 is configured to implement one or more aspects of the various aspects described in this document.

[0111] The system 400 includes at least one processor 410 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this document. The processor 410 may include embedded memory, input / output interfaces, and various other circuits as known in the art. The system 400 includes at least one memory 420 (e.g., a volatile memory device and / or a non-volatile memory device). The system 400 includes a storage device 440, which may include non-volatile memory and / or volatile memory, including but not limited to electrically erasable programmable read-only memory (EEPROM), read-only memory (ROM), programmable read-only memory (PROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, magnetic disk drive, and / or optical disk drive. As non-limiting examples, the storage device 440 may include an internal storage device, an attached storage device (including removable and non-removable storage devices), and / or a network-accessible storage device.

[0112] The system 400 includes an encoder / decoder module 430, which is configured to process data to provide encoded video or decoded video, and the encoder / decoder module 430 may include its own processor and memory. The encoder / decoder module 430 represents a module (one or more) that can be included in a device to perform encoding and / or decoding functions. As is well known, a device may include one or both of an encoding module and a decoding module. In addition, the encoder / decoder module 430 may be implemented as a separate element of the system 400, or may be incorporated into the processor 410 as a combination of hardware and software as known to those skilled in the art.

[0113] Program code to be loaded onto the processor 410 or the encoder / decoder 430 to perform various aspects described in this document may be stored in the storage device 440 and subsequently loaded onto the memory 420 for execution by the processor 410. According to various embodiments, one or more of the processor 410, the memory 420, the storage device 440, and the encoder / decoder module 430 may store one or more of the various entries during execution of the processes described in this document. Such stored entries may include, but are not limited to, input video, decoded video or portions of decoded video, bitstreams, matrices, variables, and intermediate or final results from processing equations, formulas, operations, and operational logic.

[0114] In some embodiments, memory internal to the processor 410 and / or encoder / decoder module 430 is used to store instructions and provide working memory for processing during encoding or decoding. However, in other embodiments, memory external to the processing device (e.g., the processing device may be the processor 410 or the encoder / decoder module 430) is used for one or more of these functions. The external memory may be memory 420 and / or storage device 440, such as dynamic volatile memory and / or non-volatile flash memory. In some embodiments, the external non-volatile flash memory is used to store, for example, the operating system of the television. In at least one embodiment, a fast external dynamic volatile memory (such as RAM) is used as working memory for video encoding and decoding operations such as, for example, MPEG-2 (MPEG refers to Moving Picture Experts Group, MPEG-2 is also known as ISO / IEC 13818, and 13818-1 is also known as H.222, and 13818-2 is also known as H.262), HEVC (HEVC refers to High Efficiency Video Coding, also known as H.265 and MPEG-H Part 2), or VVC (Versatile Video Coding, a new standard developed by the Joint Video Experts Team (JVET).

[0115] Input to the elements of system 400 may be provided through various input devices as indicated in block 445. Such input devices include, but are not limited to: (i) a radio frequency (RF) section that receives an RF signal transmitted over the air, for example, by a broadcaster; (ii) a component (COMP) input terminal (or a set of COMP input terminals); (iii) a universal serial bus (USB) input terminal; and / or (iv) a high-definition multimedia interface (HDMI) input terminal. Figure 4 Other examples not shown include composite video.

[0116] In various embodiments, the input device of block 445 has associated corresponding input processing elements as known in the art. For example, the RF part can be associated with elements suitable for the following: (i) selecting the desired frequency (also referred to as selecting a signal, or limiting the signal band to a frequency band), (ii) down-converting the selected signal, (iii) again band-limiting to a narrower frequency band to select a signal band that (for example) can be referred to as a channel in some embodiments, (iv) demodulating the down-converted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired data packet stream. The RF part of various embodiments includes one or more elements for performing these functions, such as a frequency selector, a signal selector, a band limiter, a channel selector, a filter, a down-converter, a demodulator, an error corrector, and a demultiplexer. The RF part can include a tuner that performs various of these functions, including, for example, down-converting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or down-converting it to baseband. In a set-top box embodiment, the RF part and its associated input processing element receive the RF signal transmitted by wired (for example, cable) medium, and by filtering, down-conversion and filtering to desired frequency band again to perform frequency selection.Various embodiments rearrange the order of (and other) element described above, remove some elements in these elements, and / or add other elements of execution similar or different functions.Adding element can be included in and inserts element between existing element, such as, inserts amplifier and analog to digital converter.In various embodiments, the RF part comprises antenna.

[0117] In addition, the USB and / or HDMI terminals may include corresponding interface processors for connecting the system 400 to other electronic devices across the USB and / or HDMI connections. It is understood that various aspects of input processing (e.g., Reed-Solomon error correction) may be implemented, for example, within a separate input processing IC or within the processor 410, as desired. Similarly, various aspects of USB or HDMI interface processing may be implemented, for example, within a separate interface IC or within the processor 410, as desired. The demodulated, error-corrected, and demultiplexed streams are provided to various processing elements, including, for example, the processor 410 and the encoder / decoder 430, which operate in conjunction with memory and storage elements to process the data streams as needed for presentation on an output device.

[0118] The various elements of system 400 may be provided within an integrated housing within which the various elements may be interconnected and data transferred therebetween using a suitable connection arrangement 425 (e.g., an internal bus as known in the art, including an inter-IC (I2C) bus, wiring, and printed circuit boards).

[0119] System 400 includes a communication interface 450 that enables communication with other devices via a communication channel 460. Communication interface 450 may include, but is not limited to, a transceiver configured to transmit and receive data over communication channel 460. Communication interface 450 may include, but is not limited to, a modem or a network card, and communication channel 460 may be implemented, for example, within a wired and / or wireless medium.

[0120] In various embodiments, a wireless network, such as a Wi-Fi network (e.g., IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers)), is used to stream or otherwise provide data to the system 400. The Wi-Fi signals of these examples are received over a communication channel 460 and a communication interface 450 suitable for Wi-Fi communications. The communication channel 460 of these embodiments is typically connected to an access point or router that provides access to external networks, including the Internet, to allow streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 400 using a set-top box that delivers data through the HDMI connection of the input block 445. Still other embodiments provide streamed data to the system 400 using the RF connection of the input block 445. As indicated above, various embodiments provide data in a non-streaming manner. In addition, various embodiments use wireless networks other than Wi-Fi, such as cellular networks or Bluetooth networks.

[0121] The system 400 can provide output signals to various output devices (including a display 475, a speaker 485, and other peripheral devices 495). The display 475 of various embodiments includes, for example, one or more of a touch screen display, an organic light emitting diode (OLED) display, a curved display, and / or a foldable display. The display 475 can be used for a television, a tablet computer, a laptop computer, a cellular phone (mobile phone), or other devices. The display 475 can also be integrated with other components (for example, as in a smartphone), or separated (for example, an external monitor for a laptop computer). In various examples of embodiments, the other peripheral devices 495 include one or more of a stand-alone digital video disk (or digital versatile disk) (DVR, for both terms), a disk player, a stereo system, and / or a lighting system. Various embodiments use one or more peripheral devices 495 that provide functions based on the output of the system 400. For example, a disk player performs the function of playing the output of the system 400.

[0122] In various embodiments, control signals are communicated between the system 400 and a display 475, speaker 485, or other peripheral device 495 using signaling such as AV.Link, Consumer Electronics Control (CEC), or other communication protocols that enable device-to-device control with or without user intervention. Output devices can be communicatively coupled to the system 400 via dedicated connections through respective interfaces 470, 480, and 490. Alternatively, output devices can be connected to the system 400 using a communication channel 460 via a communication interface 450. In an electronic device such as, for example, a television, the display 475 and speaker 485 can be integrated into a single unit with the other components of the system 400. In various embodiments, the display interface 470 includes a display driver, such as, for example, a timing controller (TCon) chip.

[0123] For example, if the RF portion of input 445 is part of a separate set-top box, the display 475 and speaker 485 may alternatively be separate from one or more of the other components. In various embodiments where the display 475 and speaker 485 are external components, the output signal may be provided via a dedicated output connection, including, for example, an HDMI port, a USB port, or a COMP output.

[0124] The embodiments may be executed by computer software implemented by the processor 410, or by hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments may be implemented by one or more integrated circuits. As a non-limiting example, the memory 420 may be of any type suitable for the technical environment and may be implemented using any appropriate data storage technology (such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory). As a non-limiting example, the processor 410 may be of any type suitable for the technical environment and may encompass one or more of a microprocessor, a general-purpose computer, a special-purpose computer, and a processor based on a multi-core architecture.

[0125] Various implementations involve decoding. As used herein, "decoding" may encompass, for example, all or part of a process performed on a received coded sequence to produce a final output suitable for display. In various embodiments, such a process includes one or more of the processes typically performed by a decoder, such as entropy decoding, inverse quantization, inverse transform, and differential decoding. In various embodiments, such a process also includes or alternatively includes processes performed by the decoder of the various implementations described herein, such as determining a local illumination compensation (LIC) model for a block; obtaining a LIC parameter set based on the LIC model determined for the block; and decoding the block based on the LIC parameter set. A block may be, for example, a coded block.

[0126] As a further example, in one example, "decoding" refers only to entropy decoding, in another embodiment, "decoding" refers only to differential decoding, and in another embodiment, "decoding" refers to a combination of entropy decoding and differential decoding. Whether the phrase "decoding process" is intended to refer specifically to a subset of operations or generally to a broader decoding process will be clear based on the context of the specific description and is believed to be well understood by those skilled in the art.

[0127] Various implementations involve encoding. In a manner similar to the discussion above regarding "decoding," "encoding," as used herein, can encompass all or part of the processes performed on an input video sequence to produce an encoded bitstream, for example. In various embodiments, such processes include one or more of the processes typically performed by an encoder, such as segmentation, differential encoding, transforms, quantization, and entropy encoding. In various embodiments, such processes also include, or alternatively include, processes performed by the encoder of the various implementations described herein, such as selecting a LIC model from multiple LIC models for a block; obtaining a LIC parameter set based on the LIC model selected for the block; and encoding the block based on the LIC parameter set. A block can be, for example, a coding block.

[0128] As a further example, in one embodiment, "encoding" refers only to entropy encoding, in another embodiment, "encoding" refers only to differential encoding, and in another embodiment, "encoding" refers to a combination of differential encoding and entropy encoding. Whether the phrase "encoding process" is intended to refer specifically to a subset of operations or generally to a broader encoding process will be clear based on the context of the specific description and is believed to be well understood by those skilled in the art.

[0129] Note that the syntax elements used herein (e.g., LIC_model, filter_idx, grad_pattern_idx, etc.) are descriptive terms and therefore do not preclude the use of other syntax element names.

[0130] When the figures are presented as flow charts, it should be understood that they also provide block diagrams of the corresponding apparatus. Similarly, when the figures are presented as block diagrams, it should be understood that they also provide flow charts of the corresponding methods / processes.

[0131] Various embodiments relate to rate-distortion optimization. In particular, during the encoding process, a balance or trade-off between rate and distortion is often considered, often within computational complexity constraints. Rate-distortion optimization is often expressed as minimizing a rate-distortion function, which is a weighted sum of rate and distortion. There are different approaches to solving the rate-distortion optimization problem. For example, an approach can be based on extensive testing of all coding options (including all considered modes or coding parameter values), with a complete evaluation of their coding costs and the associated distortion of the reconstructed signal after encoding and decoding. Faster approaches can also be used to avoid coding complexity, particularly by calculating approximate distortion based on a prediction or prediction residual signal rather than a reconstructed residual signal. A hybrid of these two approaches can also be used, such as by using approximate distortion for only some of the possible coding options and full distortion for others. Other approaches only evaluate a subset of the possible coding options. More generally, many approaches employ any of a variety of techniques to perform optimization, but optimization does not necessarily require a complete evaluation of both coding costs and associated distortion.

[0132] The implementations and aspects described herein can be implemented in, for example, a method or process, a device, a software program, a data stream, or a signal. Even if discussed only in the context of a single form of implementation (e.g., discussed only as a method), the implementation of the features discussed can also be implemented in other forms (e.g., a device or program). The device can be implemented in, for example, appropriate hardware, software, and firmware. The method can be implemented in, for example, a processor, which generally refers to a processing device, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. The processor also includes communication equipment, such as, for example, a computer, a cellular phone, a portable / personal digital assistant ("PDA"), and other equipment that facilitates the transmission of information between end users.

[0133] References to "one embodiment," "an embodiment," "an example," "an implementation," or "an implementation," and variations thereof, mean that a particular feature, structure, characteristic, etc. described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "in an example," "in an implementation," or "in an implementation," and any other variations thereof, in various places throughout this application are not necessarily all referring to the same embodiment or example.

[0134] Furthermore, the present application may involve "determining" various pieces of information. Determining information may include, for example, one or more of: estimating information, calculating information, predicting information, or retrieving information from a memory. Obtaining information may include receiving, retrieving, constructing, generating, and / or determining.

[0135] Furthermore, the present application may involve "accessing" various pieces of information. Accessing information may include, for example, one or more of: receiving information, retrieving information (e.g., retrieving information from a memory), storing information, moving information, copying information, calculating information, determining information, predicting information, or estimating information.

[0136] Furthermore, the present application may involve "receiving" various pieces of information. As with "accessing," receiving is intended to be a broad term. Receiving information may include, for example, one or more of: accessing information or retrieving information (e.g., retrieving information from a memory). Furthermore, "receiving" is generally referred to in one way or another during operations such as, for example, storing information, processing information, transmitting information, moving information, copying information, erasing information, calculating information, determining information, predicting information, or estimating information.

[0137] It is to be understood that, for example, in the case of "A / B," "A and / or B," and "at least one of A and B," use of any of the following " / ," "and / or," and "at least one of..." is intended to encompass selecting only the first-listed option (A), or only the second-listed option (B), or both options (A and B). As a further example, in the case of "A, B, and / or C" and "at least one of A, B, and C," such wording is intended to encompass selecting only the first-listed option (A), or only the second-listed option (B), or only the third-listed option (C), or only the first-listed option and the second-listed option (A and B), or only the first-listed option and the third-listed option (A and C), or only the second-listed option and the third-listed option (B and C), or all three options (A, B, and C). As will be apparent to one of ordinary skill in this and related arts, this can be extended to as many items as listed.

[0138] In addition, as used herein, the term "signaling" refers to, among other things, an indication of something to a corresponding decoder. Thus, in an embodiment, the same parameters are used on both the encoder and decoder sides. Thus, for example, the encoder can transmit (explicitly signal) specific parameters to the decoder so that the decoder can use the same specific parameters. Conversely, if the decoder already has specific parameters and other parameters, signaling can be used without transmitting (implicitly signaling) to allow only the decoder to know and select the specific parameters. By avoiding transmitting any actual functionality, bit savings are achieved in various embodiments. It should be understood that signaling can be implemented in a variety of ways. For example, in various embodiments, information is signaled to the corresponding decoder using one or more grammatical elements, flags, and the like. Although the verb form of the term "signaling" is mentioned above, the term "signal" can also be used as a noun in this article.

[0139] As will be apparent to one of ordinary skill in the art, implementations can generate a variety of signals formatted to carry information that can be stored or transmitted, for example. The information can include, for example, instructions for performing a method or data generated by one of the described implementations. For example, a signal can be formatted to carry a bitstream of the described embodiments. Such a signal can be formatted as, for example, an electromagnetic wave (e.g., using the radio frequency portion of the spectrum) or as a baseband signal. Formatting can include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information carried by the signal can be, for example, analog or digital information. As is well known, signals can be transmitted over a variety of different wired or wireless links. The signal can be stored on a processor-readable medium.

[0140] Many embodiments are described herein. The features of an embodiment may be provided individually or in any combination across various claim categories and types. In addition, an embodiment may include one or more of the features, devices, or aspects described herein individually or in any combination across various claim categories and types. For example, the features described herein may be implemented in a bitstream or signal including information generated as described herein. This information may allow a decoder to decode a bitstream, encoder, bitstream, and / or decoder according to any of the described embodiments. For example, the features described herein may be implemented by creating and / or transmitting and / or receiving a bitstream or signal and / or decoding a bitstream or signal. For example, the features described herein may be implemented by a method, process, device, medium storing instructions, medium storing data, or signal. For example, the features described herein may be implemented by a TV, set-top box, cell phone, tablet computer, or other electronic device that performs decoding. The TV, set-top box, cell phone, tablet computer, or other electronic device may display (e.g., using a monitor, screen, or other type of display) the resulting image (e.g., an image reconstructed from the residual of the video bitstream). The TV, set-top box, cell phone, tablet computer, or other electronic device may receive a signal including an encoded image and perform decoding.

[0141]

[0014] Feature(s) associated with local illumination compensation (LIC) are provided herein.

[0142] LIC is a coding tool that can be used to address the problem of local illumination changes between temporally adjacent pictures in some inter-frame prediction processes. Blocks can be decoded based on a set of LIC parameters. For example, LIC can be based on a linear model where LIC parameters (e.g., scaling factor α and offset β) are applied to reference samples to obtain predicted samples for the current block. LIC can be mathematically modeled by the following equation:

[0143] P(x,y)=α*P r (x+v x ,y+v y )+β

[0144] Where P(x, y) represents the prediction signal of the current block at the coordinate (x, y); P r (x+v x , y+v y ) represents the motion vector (v x , v y ) is a prediction signal of a reference block obtained by ; and α and β represent the corresponding scaling factor and offset applied to the reference block, respectively.

[0145] The device may obtain a set of LIC parameters associated with a LIC model (eg, a linear or polynomial LIC model) of the block. Figure 5 An example LIC parameter estimation process is shown. Figure 5 In

[15] , when LIC is applied to a block, a minimum mean square error (LMSE) method can be used to reconstruct adjacent samples (e.g., samples in the left column and upper row of the current block, e.g., Figure 5 The template T in the current block) and the corresponding adjacent samples in the reference block of the current block (e.g., Figure 5 The values ​​of the LIC parameters (e.g., α and β) are derived by minimizing the difference between T0 or T1 in

[0146]

[0147] Where N represents the number of template samples used to derive LIC parameters; T(x i ,y i ) indicates that the current block is at coordinate (x i ,y i ) at the template sample; and Represents the motion vector based on the current block The template samples and the reference template samples can be subsampled (e.g., 2:1 subsampling, e.g., to reduce computational complexity) to derive LIC parameters for block sizes larger than 8x8 (e.g., using only Figure 5 to derive α and β from the shadow samples in .

[0148] LIC can be applied in sub-block mode (e.g., affine mode). In sub-block mode, LIC parameters can be derived based on template samples derived on a sub-block basis, such as Figure 6 As shown in . The reference samples in the top template can be obtained by the sub-block MVs in the top row (e.g., each sub-block MV), and the reference samples in the left template can be obtained by the sub-block MVs in the left column (e.g., each sub-block MV). The derivation of the LIC parameters can remain unchanged.

[0149] LIC parameters α and β can be calculated at the encoder and decoder based on the current block template T and the reference block template T 0 / 1 Export.

[0150] If inter blocks are predicted using merge mode, the LIC flag may be included as part of the motion information (e.g., in addition to the motion vector predictor (MVP) and reference index). If a merge candidate list is constructed, the LIC flag may be inherited from the neighboring blocks of the merge candidate. In some examples (e.g., when inter blocks are predicted using non-merge mode), the LIC flag may be context-coded using a single context. If the LIC tool is not applicable, the LIC flag may not be signaled.

[0151] LIC may be applied to both luma and chroma components with one or more of the following configurations: LIC is disabled for combined inter / intra prediction (CIIP) and intra block copy (IBC) blocks; LIC is disabled for blocks with fewer than 32 luma samples; there is no temporal inheritance of the LIC flag; no pruning based on the LIC flag is performed in merge candidate list generation; LIC is not applied to bi-directional prediction; and / or samples of the reference block template are generated by using motion compensation (MC) with block MV without rounding them to integer pixel precision.

[0152]

[0014] Feature(s) associated with a Cross-Component Linear Model (CCLM) are provided herein.

[0014] Feature(s) associated with an enhanced CCLM are provided herein.

[0153] CCLM chroma intra prediction can be used to exploit the relationship between luma and chroma components. Chroma samples can be predicted based on the reconstructed luma samples of the same coding unit (CU) using the following linear model:

[0154] Pred c (x,y)=α*Rec L ′(x,y)+β

[0155] Among them, Pred c (x, y) represents the predicted chroma sample in the CU at coordinate (x, y); and Rec L '(x, y) represents the downsampled reconstructed luma samples of a CU (eg, the same CU). Parameters α and β can be derived from the reconstructed samples around the current block.

[0156] CCLM can use the LMSE method (e.g., similar to LIC) between adjacent reconstructed downsampled luma samples and causal chroma samples to derive model parameters α and β:

[0157]

[0158] Where I represents the total number of samples of adjacent data; and Rec c (x i ,y i) represents the reconstructed chroma samples around the target CU.

[0159] like Figure 7 As illustrated in , left and above causal samples (e.g., marked as gray circles) may be involved in the calculation to keep the number of total samples I to be a power of 2. For a target N×N chroma block, if both left and above causal samples are available (e.g., both are available), the total number of involved samples is 2N. For a target N×N chroma block, if either the left causal sample or the above causal sample (e.g., only the left causal sample or the above causal sample) is available, the total number of involved samples is N.

[0160] Figure 8 An example of a linear relationship solved by linear regression is shown. Figure 8 A point on the graph of may correspond to a pair of luma and chroma samples (Y, C).

[0161]

[0014] Feature(s) associated with a convolutional cross-component model (CCCM) are provided herein.

[0162] CCCM can predict chroma samples from reconstructed luma samples (e.g., in a similar manner to CCLM). When chroma subsampling is used, the reconstructed luma samples can be downsampled to match the lower resolution chroma grid (e.g., as done in CCLM).

[0163] The convolutional 7-tap filter may include a 5-tap plus-shaped spatial component, a nonlinear term, and a bias term. The input to the spatial 5-tap component of the filter may include a center (C) luma sample (e.g., which may be concatenated with the chroma sample to be predicted) and its upper / north (N), lower / south (S), left / west (W), and right / east (E) neighboring samples, as Figure 9A The output of the filter can be calculated as the filter coefficient c i The convolution between the chroma and input values ​​is performed and is clipped to a certain range of chroma samples (e.g., valid chroma samples):

[0164] Pred c =c0*C+c1*N+c2*S+c3*E+c4*W+c5*P+c6*B

[0165] where the nonlinear term P represents the power of two of the center luma sample C (e.g., and scaled to the sample value range of the content), and the bias term B represents a scalar offset between the input and output (e.g., similar to the offset term used in CCLM). The bias term B can be set to the intermediate chroma value. For example, for 10-bit content, the nonlinear term P and the bias term B can be calculated as:

[0166] P=(C*C+512)>>10,

[0167] B=512.

[0168] The filter coefficients c can be calculated by minimizing the MSE between the predicted and reconstructed chroma samples in the reference region i . Figure 9B An example reference region is shown, comprising six rows of chroma samples above and to the left of the block. The reference region may extend one block width to the right and one block height below the block boundary.

[0169] MSE minimization can be performed by calculating the autocorrelation matrix of the luma input and the cross-correlation vector between the luma input and the chroma output. The autocorrelation matrix can be LDL decomposed and back-substitution can be used to calculate the final filter coefficients. This process can follow (e.g., approximately follow) the calculation of the adaptive loop filter (ALF) coefficients. LDL decomposition can be used (e.g., instead of Cholesky decomposition) to avoid the use of square root operations. For example, integer arithmetic (e.g., only integer arithmetic) can be used in MSE minimization.

[0170] The encoder may perform rate-distortion (RD) checking in the chroma prediction mode loop. CCCM may be considered a sub-mode of CCLM (e.g., in terms of signaling). The use of CCCM mode may be signaled using a PU-level flag encoded with context-adaptive binary arithmetic coding (CABAC).

[0171] Feature(s) associated with filter-based linear models (FLMs) are provided herein.

[0172] FLM can be used to improve CCLM. FLM can extend the simple linear regression (SLR) in CCLM to multiple linear regression (MLR), which can be expressed as:

[0173]

[0174] Among them, Pred C Represents the chrominance sample to be predicted; Rec L’i represents the i-th reconstructed luma sample around the chroma sample; α i denotes the i-th coefficient; β denotes the offset; and N denotes the number of luma samples involved.

[0175] like Figure 10As illustrated in FIG, multiple adjacent luma and chroma template samples (e.g., the same number of adjacent luma / chroma template samples as in CCLM, e.g., top 2 rows / left 3 columns of luma samples and top 1 row / left 1 column of chroma samples) can be used to derive MLR parameters (e.g., via Cholesky decomposition). In some FLM designs, multiple filter shapes can be supported (e.g., where the number N of filter shapes ranges from, for example, 2 to 6). The selection of the filter shape can be performed at the CU level (e.g., switched).

[0176] Provided herein are feature(s) associated with gradient linear models (GLMs).

[0177] A GLM can use the gradient of luma samples to derive a linear model (e.g., instead of using downsampled luma values, as is done in CCLM). For example, the gradient G can be used in a CCLM process (e.g., instead of low-pass filtered luma samples):

[0178] pred C (x,y)=α*G(x,y)+β

[0179] where pred C (x, y) represents the predicted value of the chrominance sample; and G(x, y) represents the gradient of the corresponding reconstructed luma sample. The linear model parameters α and β can be derived from the neighboring reconstructed samples (e.g., based on the LMSE method, as done in CCLM). Figure 11 It illustrates that the gradient G(x,y) can be computed via a gradient mode (eg, one of four Sobel-based gradient modes).

[0180] For signaling, if CCLM mode is enabled for the current CU, one or more (e.g., two) flags (e.g., one flag for each of the Cb and Cr components) may be signaled (e.g., separately) for the Cb and Cr components. The two flags may indicate whether GLM is enabled for the Cb and / or Cr components. If GLM is enabled for a component, a syntax element may be signaled (e.g., further signaled) to select a gradient mode (e.g., one of four gradient modes) for gradient calculation, such as Figure 11 As shown in the figure.

[0181] Provided herein are feature(s) associated with a GLM having a luminance value.

[0182] GLM mode (where the gradient of the luma sample G(x, y) and the reconstructed value Rec′ of the downsampled luma sample L (x,y)) can be used to predict chrominance samples with different parameters:

[0183] pred C (x,y)=α0*G(x,y)+α1*Rec′ L (x,y)+α2*midValue

[0184] where the model parameters α0, α1, and α2 can be derived from the six rows and six columns of neighboring samples (e.g., based on the LDL decomposition method, as done in CCCM mode).

[0185] For signaling, a flag in the bitstream can be used to signal the GLM mode (e.g., where the gradient G(x,y) of the luma samples and the reconstructed values ​​Rec′ of the downsampled luma samples are L Both (x, y) can be used to predict chroma samples) (eg, as an additional mode).

[0186] In an example, an extended model can be used to compensate for temporal and / or spatial illumination differences. For example, the device can determine to use a polynomial LIC model for a block. The extended model can be a filter-based and / or gradient-based linear or polynomial model. The extended model can be used to generate a prediction of the current block with illumination compensation.

[0187] The extended model may improve compression efficiency (eg, by reducing bitrate while maintaining quality, or by improving quality while maintaining bitrate).

[0188] The feature(s) described herein can be used to enhance coding efficiency for inter-blocks that have spatial illumination difference information that is correlated (e.g., strongly correlated) with neighboring blocks. For example, an inter-block can have spatial illumination difference information that is correlated with neighboring blocks in some gaming video content (e.g., where some illumination sources are located at a certain position in the picture and light gradually spreads across the picture). Figure 12 An example of a video game picture in which light gradually propagates across the picture is illustrated. In such a case, the block to be encoded may include some background content with brightness values ​​that gradually evolve according to spatial position.

[0189] If LIC is enabled, a linear model can be applied, which can exploit temporal illumination difference information by utilizing reference blocks in the reference frame.

[0190] CCCM, FLM, and / or GLM can be applied to chroma intra prediction. In some examples, CCCM, FLM, and / or GLM can be applied only to chroma intra prediction and may not be considered as techniques for enriching illumination difference information for inter prediction. CCCM, FLM, and / or GLM can be applied to enhance the coding efficiency of LIC (e.g., by generating and using multiple models). In the examples described herein, extended models (e.g., models other than simple linear models) can be used for LIC.

[0191] For example, a filter-based linear model, a filter-based polynomial model, a gradient-based linear model, and / or a gradient-based polynomial model may be applied to the LIC.Feature(s) associated with signaling an extended LIC model are provided herein.

[0192] Feature(s) associated with LIC with a filtering-based model are described herein.

[0193] Figure 13 An example LIC method using a simple linear model is illustrated.When LIC is enabled for an inter-predicted block (1301), reconstructed neighboring samples of the current block and its reference blocks are used to generate a template (1302).

[0194] A device (e.g., a decoder or an encoder) may determine to use a linear LIC model for a block (e.g., a current block). A set of LIC parameters associated with the linear LIC model may be obtained. For example, a linear model with a scaling factor α and an offset β may be derived using a template using a LMSE method (1303). The current block may be decoded based on the set of LIC parameters associated with the linear LIC model. For example, the linear model may be applied to reference samples to obtain prediction samples (e.g., all prediction samples) for the current block (1304) to compensate for temporal illumination changes.

[0195] For inter-frame blocks, spatial illumination differences may occur (e.g., spatial neighboring luminance gradients may be highly correlated with luminance samples in the current block). A LIC linear model that considers temporal illumination variations (e.g., a single LIC model using a simple linear model) may not be applicable to some blocks.

[0196] In an example, the linear model can be filter-based (e.g., FLM can be applied to LIC). LIC with FLM can extend simple linear regression to multiple linear regression (MLR), which can be expressed as:

[0197]

[0198] Where P(x, y) is the prediction signal of the current block at coordinate (x, y); It is based on the motion vector (v x , v y ) The i-th prediction sample around the prediction sample of the reference block obtained; α i is the i-th coefficient; β is the offset; and N is the number of prediction samples of the reference block involved.

[0199] LIC parameters may include filter coefficients (e.g., α i and β). A template sample of the current block (e.g., a neighboring sample in a reference region of the current block) may be identified. For a sample position in the current block (e.g., (x, y)), multiple reference samples of a reference block associated with the current block (e.g., The corresponding filter coefficients from the filter coefficient set (e.g., α for each of the i-th prediction samples of the reference block) can be i ) is applied to multiple neighboring samples to generate the predicted sample of the current block (e.g., P(x, y)).

[0200] Filter coefficient α i The difference (e.g., mean square error (MSE)) between the template samples of the current block (e.g., reconstructed neighboring samples in a predefined reference region (e.g., template) of the current block) and the first reference template samples in the template of the reference block corresponding to the template samples of the current block may be calculated. For example, Figure 14 As shown in FIG, template samples (eg, reconstructed neighboring samples in the top row and left column of the current block, such as Figure 14 ), and a first reference template sample corresponding to the template sample and a plurality of reference template samples adjacent to the first reference template sample (e.g., corresponding reconstructed adjacent samples in the top 2 rows and left 3 columns of the reference block, e.g. Figure 14 The template T' in the filter coefficients α are derived i .

[0201] For example, a set of filter coefficients can be derived based on minimizing the difference between a template sample of a block and a corresponding predicted template sample obtained based on a plurality of reference template samples. For example, MSE minimization can be performed by computing an autocorrelation matrix of an input having reconstructed neighboring samples of a reference block, and a cross-correlation vector between an input having reconstructed neighboring samples of the reference block and an output having reconstructed neighboring samples of the current block. The autocorrelation matrix can be subjected to an LDL or Cholesky decomposition. Back substitution can be used to compute the filter coefficients (e.g., the final filter coefficients).

[0202] The offset β can be set to an intermediate luminance value (e.g., β=512 for 10-bit content). In some examples, the offset β can be set to 0. In this case, FLM can be expressed as:

[0203]

[0204] N multiple filter shapes can be supported (e.g., N=2). For each filter shape, the number of filter coefficients can range from a first predefined value NumFilterCoeff 1st To the second predefined value NumFilterCoeff 2nd (For example, the range is from NumFilterCoeff 1st =2 to NumFilterCoeff 2nd =6). The filter shape (and number of filter coefficients) may be selected (e.g., switched) at a certain level (e.g., prediction unit (PU), coding unit (CU), coding tree unit (CTU), slice, and / or frame level).

[0205] like Figure 15 As illustrated in , the filter shape may be, for example, a 3x2 rectangular shape (eg, having six filter coefficients) and a 3x3 diamond shape (eg, having five filter coefficients).

[0206] The polynomial LIC model may be a filter-based model (e.g., a filter-based polynomial model may be applied to the LIC). For example, a convolutional 7-tap filter (e.g., which may include a 5-tap plus-shaped spatial component, a nonlinear term, and a bias term) may be applied to the LIC. A template sample of the current block may be identified. For a sample position (e.g., (x, y)) in the current block, a plurality of reference samples of a reference block associated with the block may be identified (e.g., P in the following equation). r (x+v x , y+v y ) etc.). The multiple reference samples may include a center reference sample (eg, P) corresponding to a sample position in the current block. r (x+v x , y+v y )). For example, the input to the spatial 5-tap component of the filter includes the first reference template sample in the template of the reference block of the block, which corresponds to the template sample of the block (e.g., based on the motion vector (v x , v y ) The central prediction sample of the reference block obtained: P r (x+v x , y+v y), and a plurality of second reference template samples adjacent to the first reference template sample (eg, above / north side of the first reference template sample (P r (x+v x , y+v y -1)), below / south side (P r (x+v x , y+v y +1)), left / west (P r (x+v x -1, y+v y )) and right / east side (P r (x+v x +1,y+v y ))neighbors), such as Figure 9A As shown in FIG. Corresponding prediction template samples can be obtained based on multiple second reference template samples. For example, the output of the filter can be calculated as the filter coefficient c i The convolution between the λ and the input value. The output of the filter can be clipped to the range of valid luma samples. Corresponding LIC parameters (e.g., filter coefficients) from the set of LIC parameters can be applied to multiple reference samples to generate prediction samples for the block. The LIC parameters corresponding to the center reference sample can be applied to the square of the center reference sample (e.g., P r (x+v x , y+v y ) 2 ). For example, the LIC with a filter-based polynomial model can be expressed as:

[0207] P(x, y) = c0*P r (x+v x , y+v y )+c1*P r (x+v x , y+v y -1)+c2*P r (x+v x , y+v y +1)+c3*P r (x+v x -1, y+v y )+c4*P r (x+v x +1,y+v y )+c5*(P r (x+v x , y+v y ) 2 +2 BD-1 )>>BD+c6*B

[0208] where the nonlinear term is the reference block Pr (x+v x , y+v y ) and is scaled to the sample value range of the content; the linear term corresponds to multiple second reference template samples of the reference block; the bias term B represents a scalar offset between the input and output (similar to the offset term in CCLM) and is set to the median luminance value (B = 2 BD-1 ); and BD represents the bit depth (eg, for 10-bit content, BD=10).

[0209] A set of filter coefficients (eg, filter coefficients c ) may be derived based on minimizing the difference between the template samples of the block and corresponding predicted template samples obtained based on a plurality of second reference template samples. i ). For example, the filter coefficients may be calculated by minimizing the difference (eg, MSE) between the predicted neighboring samples and the reconstructed neighboring samples in a template (eg, a predefined template) of the current block. Figure 9B As illustrated in FIG, the reconstructed neighboring samples in the top six rows and left six columns of the current block (eg, one block width to the right and one block height below the block boundary) can be used to derive the filter coefficients c i .

[0210] MSE minimization can be performed by calculating the autocorrelation matrix of the input with the predicted neighboring samples of the current block and the cross-correlation vector between the input with the predicted neighboring samples of the current block and the output with the reconstructed neighboring samples of the current block. The autocorrelation matrix can be subjected to LDL or Cholesky decomposition. Back substitution can be used to calculate the filter coefficients (e.g., the final filter coefficients).

[0211] In an example, a convolutional 11-tap filter (e.g., which may include a 9-tap plus-shape spatial component, a nonlinear term, and a bias term) may be applied to the LIC. The four components in the 9-tap filter (e.g., in addition to the input of the spatial 5-tap component of the filter described above) may include: x , v y ) The upper left / northwest (NW) neighbor of the central prediction sample of the reference block obtained: P r (x+v x -1, y+v y -1), upper right / northeast (NE) neighbor (P r (x+v x +1,y+v y -1)), lower left / southwest (SW) neighbor (P r (x+v x -1, y+v y+1)) and the lower right / southeast (SE) neighbor (P r (x+v x +1,y+v y +1)), such as Figure 16 Those skilled in the art will appreciate that other possible convolution filters (eg, other convolution filter structures) may be applied to LIC.

[0212] The number of filter taps Numtap may have a value (e.g., a predetermined value) other than 9 or 11. The number of filter taps may be a fixed value or may be selected (e.g., and switched) at a certain level (e.g., PU, ​​CU, CTU, slice, and / or frame level).

[0213] The bias term B can be set to 0. In this case, the model can be expressed as:

[0214] P(x, y) = c0*P r (x+v x , y+v y )+c1*P r (x+v x , y+v y -1)+c2*P r (x+v x , y+v y +1)+c3*P r (x+v x -1, y+v y )+c4*P r (x+v x +1,y+v y )+c5*(P r (x+v x , y+v y ) 2 +2 BD-1 )>>BD

[0215] Nonlinear terms (e.g., several nonlinear terms) may be used in a quadratic (e.g., second-order) polynomial model, such as:

[0216] P(x, y) = c0*P r (x+v x , y+v y )+c1*P r (x+v x , y+v y -1)+c2*P r (x+v x , y+v y +1)+c3*P r (x+v x-1, y+v y )+c4*P r (x+v x +1,y+v y )+c5*(P r (x+v x , y+v y ) 2 +2 BD-1 )>>BD+c6*(P r (x+v x , y+v y -1) 2 +2 BD -1 )>>BD+c7*(P r (x+v x -1, y+v y ) 2 +2 BD-1 )>>BD

[0217] Nonlinear terms (e.g., several nonlinear terms) can be used in a general polynomial model, such as:

[0218]

[0219] Provided herein are feature(s) associated with LIC with gradient-based models.

[0220] The reference sample gradient of the reference block associated with the current block (e.g., G r (x+v x , y+v y )) to derive a set of LIC parameters. For example, a gradient linear model can be applied by utilizing the gradients of reference samples from a reference block (e.g., instead of reference samples) to derive a linear model for LIC. For example, the gradient G can be used in the LIC process r :

[0221] P(x, y) = α*G r (x+v x , y+v y )+β

[0222] Where P(x, y) represents the prediction signal of the current block at the coordinate (x, y); G r (x+v x , y+v y ) represents the motion vector (v x , v y ) obtains the spatial gradient of the corresponding prediction sample of the reference block; and the linear model parameters α and β can be derived through the neighboring reconstructed samples based on the LMSE method described in this article.

[0223] The bias β can be set to 0. In this case, the gradient linear model can be expressed as:

[0224] P(x, y) = α*G r (x+v x , y+v y )

[0225] G r (x+v x , y+v y ) can be expressed based on the motion vector (v x , v y ) is the spatial gradient of the corresponding reconstructed samples of the reference block obtained.

[0226] The linear model parameters α and β can be derived from the adjacent reconstruction samples based on the minimum (Min) and maximum (Max) spatial gradient values, which simplifies the process of deriving the linear model parameters. The minimum and maximum gradient values ​​can be searched in the adjacent spatial gradients in the template of the reference block. After determining the minimum and maximum gradient samples, the minimum / maximum gradient values ​​(G RecMax , G RecMin ) and the corresponding reconstructed samples in the template of the current block (Rec Max , Rec Min ) Use the following equations to obtain the linear model parameters α and β:

[0227]

[0228] β=Rec Min -α*G RecMin

[0229] The gradient G can be calculated based on a gradient mode (e.g., one of four Sobel-based gradient modes) r (x+v x , y+v y ),like Figure 11 In some examples, the gradient mode can be selected from more than four Sobel-based gradient modes (e.g., such as 16 Sobel-based gradient modes, such as Figure 17 (pictured in the figure).

[0230] Gradient modes (e.g., other than Sobel-based gradient modes) can be used to calculate spatial gradients. For example, gradient modes based on Laplacian, Prewitt, Roberts Cross, Robinson Compass, and / or Krisch Compass can be used to calculate spatial gradients.

[0231] In the case of bidirectional prediction, LIC may be applied to the first and second reference blocks associated with the current block (e.g., two reference blocks P0 r and P1 r ). LIC parameters can be derived based on the first and second reference blocks. For example, the final bidirectional prediction P can be obtained by combining P0 r and P1 r and then refined with a set of LIC parameters (scaling factor α and offset β) to derive:

[0232] P(x, y) = α*(P0 r (x+v0 x ,y+v0 y )+P1 r (x+v1 x , y+v1 y ))+β.

[0233] In some examples, the time gradient G can be used t (x, y). The LIC parameters can be derived based on one or more temporal gradients associated with the current block. For example, the temporal gradient G t (x, y) can be derived as follows:

[0234] G t (x, y) = (P1 r (x+v1 x , y+v1 y )-P0 r (x+v0 x ,y+v0 y )) / (Poc1-Poc0)

[0235] Where Poc0 is the picture order count of reference picture 0 and Poc1 is the picture order count of reference picture 1. In some examples, including G t The term can be added to the equation, or the equation including G can be replaced r For example, the time gradient G t Can replace the combined P0 in the LIC process r and P1 r :

[0236] P(x, y) = α*G t (x+v x , y+v y )+β

[0237] If the LIC model is a gradient-based model (e.g., a polynomial gradient-based model), a set of LIC parameters can be derived based on the reference samples and the gradients of the reference samples in the template of the current block. For example, both the reference samples and the reference sample gradients can be used to derive the linear model of the LIC. In an example, different scaling factors can be applied to the reference samples and the reference sample gradients:

[0238] P(x, y) = α0*P r (x+v x , y+v y )+α1*G r (x+v x , y+v y )+α2*β

[0239] Where (x, y) represents the prediction signal of the current block at the coordinate (x, y); P r (x+v x , y+v y ) represents the motion vector (v x , v y ) The prediction signal of the reference block obtained; G(x+v x , y+v y ) indicates P r (x+v x , y+v y ). As described herein, the model parameters α0, α1, and α2 can be derived from six rows and six columns of neighboring samples based on the LDL decomposition method. The offset β can be set to an intermediate luminance value (e.g., β=512 for 10-bit content) or to zero (e.g., β=0).

[0240] In our example, the reference sample and the reference sample gradient can share the same scaling factor α:

[0241] P(x, y) = α*(P r (x+v x , y+v y )+G r (x+v x , y+v y ))+β

[0242] The linear model parameters α and β can be derived from neighboring reconstructed samples based on the LMSE method described in this paper.

[0243] In an example, a gradient-based polynomial model can be applied to LIC. For example, the gradient-based polynomial model can include a reference sample and a power of two of the reference sample gradient to derive a quadratic polynomial model as follows:

[0244] P(x, y) = α0*(Pr (x+v x , y+v y ) 2 +2 BD-1 )>>BD+α1*G r (x+v x , y+v y )+α2*β

[0245] Among them, the nonlinear term is the prediction sample P of the reference block r (x+v x , y+v y ) is a power of two and is scaled to the sample value range of the content, and BD represents the bit depth (e.g., for 10-bit content, BD=10). The set of LIC parameters (e.g., α0 and β) can be applied to the reference samples of the reference block (e.g., P r (x+v x , y+v y )) and the gradient of the reference sample (e.g., G r (x+v x , y+v y )) to obtain a refined prediction of the current block (eg, P(x, y)). The current block can be reconstructed based on the refined prediction.

[0246] Feature(s) associated with signaling an extension model are provided herein.

[0247] In an example, if LIC is enabled for an inter-predicted block, two extended LIC models may be available (e.g., in addition to a simple linear model): one with a filter-based model (i.e., LIC-FM) and the other with a gradient-based model (i.e., LIC-GM). The encoder may perform a rate-distortion optimization (RDO) check (e.g., during the refinement process for inter-prediction). For example, a syntax element (e.g., LIC_model) may be signaled (e.g., by truncating a unary code) to indicate which model (e.g., the original LIC model, the LIC-FM model, and / or the LIC-GM model) is selected to refine the prediction of the current block. If the LIC-FM model is enabled (e.g., selected), a syntax element (e.g., filter_idx) may be signaled to indicate the selected filter (e.g., one of multiple predefined filters). If the LIC-GM model is enabled, a syntax element (e.g., grad_pattern_idx) may be signaled to indicate the selected gradient mode (e.g., one of multiple predefined gradient modes) to be used to perform gradient calculations as described herein.

[0248] Figure 18An example of decoding using LIC with an extended model is illustrated. A LIC model indication (e.g., LIC_model) may be received (e.g., in video data). The LIC model indication (e.g., a LIC flag for an inter-frame prediction block) may be decoded (1801). The receiving device may determine the LIC model to use for the block based on the LIC model indication (e.g., determine to use a polynomial LIC model). For example, if LIC is enabled, the syntax LIC_model may be decoded to indicate which LIC model to apply (1802). If LIC_model is decoded as 0, the original LIC model (e.g., a simple linear model) may be applied to the block (1803-1805).

[0249] If LIC_model is decoded as 10, the LIC-FM model with filter-based model can be applied to the block. In this case, the prediction sample P of the reference block obtained based on the motion vector r (x+v x , y+v y ) and the neighboring prediction samples around the prediction sample of the reference block can also be used to generate the current prediction block. The syntax element filter_idx can be decoded to indicate the filter to be applied (e.g., a predefined filter) (1806). The template can be used to use LDL decomposition to derive a filter with multiple scaling factors α i and a linear or polynomial model with an offset β (1807). The derived model can be applied to the reference sample and its surrounding reference samples (1808) to obtain prediction samples associated with corresponding coordinates of the current block.

[0250] If LIC_model is decoded as 11, the LIC-GM model with a gradient-based model can be applied to the block. In this case, the prediction sample P of the reference block obtained based on the motion vector r (x+v x , y+v y ) and the gradient G of the prediction sample of the reference block r (x+v x , y+v y ) can also be used to generate the current prediction block. The syntax element grad_pattern_idx can be decoded to indicate the gradient mode to be applied (e.g., a predefined gradient mode) (1809). The template can be used to derive a LDL decomposition with multiple scaling factors α i and a linear or polynomial model with an offset β (1810). The derived model can be applied to the reference samples and their gradients (1811) to obtain prediction samples associated with the corresponding coordinates of the current block.

[0251] If LIC is enabled for inter-predicted blocks, an extended LIC model (eg, LIC-FM) with a filter-based model may be available (eg, in addition to the original LIC model with a simple linear model).

[0252] If LIC is enabled for inter-predicted blocks, an extended LIC model (eg, LIC-GM) with a gradient-based model may be available (eg, in addition to the original LIC model with a simple linear model).

[0253] Although features and elements are described above in particular combinations, it will be understood by those skilled in the art that each feature or element can be used alone or in combination with other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware that is incorporated into a computer-readable medium for execution by a computer or processor. Examples of computer-readable media include electronic signals (transmitted via a wired or wireless connection) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media (such as internal hard disks and removable disks), magneto-optical media, and optical media (such as CD-ROM disks and digital versatile disks (DVDs)). A processor associated with the software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

1. A device for video decoding, comprising: A processor configured to: Determine the use of a polynomial local illumination compensation (LIC) model for the block; obtaining a set of LIC parameters associated with a polynomial LIC model for the block; and The block is decoded based on the set of LIC parameters.

2. The device according to claim 1, wherein The processor is further configured to: A LIC model indication is received in the video data, wherein the processor is configured to determine to use a polynomial LIC model for the block based on the LIC model indication.

3. The device according to claim 1, wherein The polynomial LIC model is a filter-based model, and the processor is further configured to: identifying a template sample of the block; identifying, in a template of a reference block of the block, a first reference template sample corresponding to a template sample of the block; identifying a plurality of second reference template samples adjacent to the first reference template sample; as well as A set of filter coefficients is derived based on minimizing differences between template samples of the block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

4. The apparatus according to claim 1, wherein The polynomial LIC model is a filter-based model, and the processor is further configured to: for a sample position in the block, identifying a plurality of reference samples of a reference block associated with the block, wherein the plurality of reference samples includes a center reference sample corresponding to the sample position; as well as Respective LIC parameters from the set of LIC parameters are applied to a plurality of reference samples to generate predicted samples of the block, wherein the LIC parameters corresponding to a center reference sample are applied to a square of the center reference sample.

5. The apparatus according to claim 1, wherein The polynomial LIC model is a gradient-based model, and the processor is configured to obtain the set of LIC parameters including: the processor is configured to: The set of LIC parameters is derived based on reference samples in a template of the block and gradients of the reference samples.

6. The apparatus according to claim 1, wherein The processor being configured to obtain the set of LIC parameters includes the processor being configured to derive the set of LIC parameters based on at least one of the following: a first reference block and a second reference block associated with said block; or A temporal gradient associated with the block.

7. The apparatus according to claim 1, wherein The block is a first block, the set of LIC parameters is a first set of LIC parameters, and the processor is further configured to: Determine to use the linear LIC model for the second block; obtaining a second set of LIC parameters associated with the linear LIC model of the second block; and The second block is decoded based on the second set of LIC parameters.

8. The apparatus according to claim 7, wherein The linear LIC model is a filter-based model, and the processor is further configured to: Identify the template sample of the second block; identifying, in the template of the reference block of the second block, a first reference template sample corresponding to the template sample of the second block; identifying a plurality of second reference template samples adjacent to the first reference template sample; as well as A set of filter coefficients is derived based on minimizing differences between template samples of the second block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

9. The apparatus according to claim 7, wherein The linear LIC model is a filter-based model comprising a set of filter coefficients, and the processor is further configured to: For a sample position in the second block, identifying a plurality of reference samples of a reference block associated with the second block; and Corresponding filter coefficients from the set of filter coefficients are applied to a plurality of neighboring samples to generate predicted samples of a second block.

10. The apparatus according to claim 7, wherein The linear LIC model is a gradient-based model, and the processor being configured to obtain a second set of LIC parameters includes the processor being configured to derive the second set of LIC parameters based on gradients of reference samples of a reference block associated with the second block.

11. The apparatus according to claim 1, wherein The polynomial LIC model is a gradient-based model, and the processor is further configured to: applying the set of LIC parameters to reference samples of a reference block and gradients of the reference samples to obtain a refined prediction of the block; and The block is reconstructed based on the refined prediction of the block.

12. A device for video encoding, comprising: A processor configured to: Determine the use of a polynomial local illumination compensation (LIC) model for the block; obtaining a set of LIC parameters associated with a polynomial LIC model for the block; and The block is encoded based on the set of LIC parameters.

13. The apparatus according to claim 12, wherein The processor is further configured to: A LIC model indication is sent in the video data, wherein the LIC model indication indicates use of a polynomial LIC model for the block.

14. The apparatus according to claim 12, wherein The polynomial LIC model is a filter-based model, and the processor is further configured to: identifying a template sample of the block; identifying, in a template of a reference block of the block, a first reference template sample corresponding to a template sample of the block; identifying a plurality of second reference template samples adjacent to the first reference template sample; as well as A set of filter coefficients is derived based on minimizing differences between template samples of the block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

15. The apparatus according to claim 12, wherein The polynomial LIC model is a filter-based model, and the processor is further configured to: for a sample position in the block, identifying a plurality of reference samples of a reference block associated with the block, wherein the plurality of reference samples includes a center reference sample corresponding to the sample position; as well as Respective LIC parameters from the set of LIC parameters are applied to a plurality of reference samples to generate predicted samples of the block, wherein the LIC parameters corresponding to a center reference sample are applied to a square of the center reference sample.

16. The apparatus according to claim 12, wherein The polynomial LIC model is a gradient-based model, and the processor is configured to obtain the set of LIC parameters including: the processor is configured to: The set of LIC parameters is derived based on reference samples in a template of the block and gradients of the reference samples.

17. The apparatus according to claim 12, wherein: The processor being configured to obtain the set of LIC parameters includes the processor being configured to derive the set of LIC parameters based on at least one of the following: a first reference block and a second reference block associated with said block; or A temporal gradient associated with the block.

18. The apparatus according to claim 12, wherein The block is a first block, the set of LIC parameters is a first set of LIC parameters, and the processor is further configured to: Determine to use the linear LIC model for the second block; obtaining a second set of LIC parameters associated with the linear LIC model of the second block; and The second block is encoded based on the second set of LIC parameters.

19. The apparatus according to claim 18, wherein The linear LIC model is a filter-based model, and the processor is further configured to: Identify the template sample of the second block; identifying, in the template of the reference block of the second block, a first reference template sample corresponding to the template sample of the second block; identifying a plurality of second reference template samples adjacent to the first reference template sample; as well as A set of filter coefficients is derived based on minimizing differences between template samples of the second block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

20. The apparatus of claim 18, wherein The linear LIC model is a filter-based model comprising a set of filter coefficients, and the processor is further configured to: For a sample position in the second block, identifying a plurality of reference samples of a reference block associated with the second block; and Corresponding filter coefficients from the set of filter coefficients are applied to a plurality of neighboring samples to generate predicted samples of a second block.

21. The apparatus of claim 18, wherein The linear LIC model is a gradient-based model, and the processor being configured to obtain a second set of LIC parameters includes the processor being configured to derive the second set of LIC parameters based on gradients of reference samples of a reference block associated with the second block.

22. The apparatus of claim 12, wherein: The polynomial LIC model is a gradient-based model, and the processor is further configured to: applying the set of LIC parameters to reference samples of a reference block and gradients of the reference samples to obtain a refined prediction of the block; as well as The block is encoded based on the refined prediction of the block.

23. A method for video decoding, comprising: Determine the use of a polynomial local illumination compensation (LIC) model for the block; obtaining a set of LIC parameters associated with a polynomial LIC model of the block; as well as The block is decoded based on the set of LIC parameters.

24. The method of claim 23, wherein the method further comprises: A LIC model indication is received in the video data, wherein determining to use a polynomial LIC model for the block is determined based on the LIC model indication.

25. The method according to claim 23, wherein The polynomial LIC model is a filter-based model, and the method further comprises: identifying a template sample of the block; identifying, in a template of a reference block of the block, a first reference template sample corresponding to a template sample of the block; identifying a plurality of second reference template samples adjacent to the first reference template sample; and A set of filter coefficients is derived based on minimizing differences between template samples of the block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

26. The method according to claim 23, wherein The polynomial LIC model is a filter-based model, and the method further comprises: for a sample position in the block, identifying a plurality of reference samples of a reference block associated with the block, wherein the plurality of reference samples includes a center reference sample corresponding to the sample position; and Respective LIC parameters from the set of LIC parameters are applied to a plurality of reference samples to generate predicted samples of the block, wherein the LIC parameters corresponding to a center reference sample are applied to a square of the center reference sample.

27. The method according to claim 23, wherein The polynomial LIC model is a gradient-based model, and determining the set of LIC parameters includes: The set of LIC parameters is derived based on reference samples in a template of the block and gradients of the reference samples.

28. The method according to claim 23, wherein Obtaining the set of LIC parameters includes deriving the set of LIC parameters based on at least one of the following: a first reference block and a second reference block associated with said block; or A temporal gradient associated with the block.

29. The method according to claim 23, wherein The block is a first block, the set of LIC parameters is a first set of LIC parameters, and the method further comprises: Determine to use the linear LIC model for the second block; obtaining a second set of LIC parameters associated with the linear LIC model of the second block; and The second block is decoded based on the second set of LIC parameters.

30. The method according to claim 29, wherein The linear LIC model is a filter-based model, and the method further comprises: Identify the template sample of the second block; identifying, in the template of the reference block of the second block, a first reference template sample corresponding to the template sample of the second block; identifying a plurality of second reference template samples adjacent to the first reference template sample; and A set of filter coefficients is derived based on minimizing differences between template samples of the second block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

31. The method according to claim 29, wherein The linear LIC model is a filter-based model comprising a set of filter coefficients, and the method comprises: For a sample position in the second block, identifying a plurality of reference samples of a reference block associated with the second block; and Corresponding filter coefficients from the set of filter coefficients are applied to a plurality of neighboring samples to generate predicted samples of a second block.

32. The method of claim 29, wherein: The linear LIC model is a gradient-based model, and obtaining the second set of LIC parameters includes deriving the second set of LIC parameters based on gradients of reference samples of a reference block associated with the second block.

33. The method according to claim 23, wherein The polynomial LIC model is a gradient-based model, and the method further comprises: applying the set of LIC parameters to reference samples of a reference block and gradients of the reference samples to obtain a refined prediction of the block; and The block is reconstructed based on the refined prediction of the block.

34. A method for video encoding, comprising: Determine the use of a polynomial local illumination compensation (LIC) model for the block; obtaining a set of LIC parameters associated with a polynomial LIC model of the block; as well as The block is encoded based on the set of LIC parameters.

35. The method of claim 34, wherein the method further comprises: A LIC model indication is sent in the video data, wherein the LIC model indication indicates use of a polynomial LIC model for the block.

36. The method of claim 34, wherein: The polynomial LIC model is a filter-based model, and the method further comprises: identifying a template sample of the block; identifying, in a template of a reference block of the block, a first reference template sample corresponding to a template sample of the block; identifying a plurality of second reference template samples adjacent to the first reference template sample; and A set of filter coefficients is derived based on minimizing differences between template samples of the block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

37. The method of claim 34, wherein: The polynomial LIC model is a filter-based model, and the method further comprises: for a sample position in the block, identifying a plurality of reference samples of a reference block associated with the block, wherein the plurality of reference samples includes a center reference sample corresponding to the sample position; and Respective LIC parameters from the set of LIC parameters are applied to a plurality of reference samples to generate predicted samples of the block, wherein the LIC parameters corresponding to a center reference sample are applied to a square of the center reference sample.

38. The method of claim 34, wherein: The polynomial LIC model is a gradient-based model, and obtaining the set of LIC parameters includes: The set of LIC parameters is derived based on reference samples in a template of the block and gradients of the reference samples.

39. The method of claim 34, wherein: Obtaining the set of LIC parameters includes deriving the set of LIC parameters based on at least one of the following: a first reference block and a second reference block associated with said block; or A temporal gradient associated with the block.

40. The method of claim 34, wherein The block is a first block, the set of LIC parameters is a first set of LIC parameters, and the method further comprises: Determine to use the linear LIC model for the second block; obtaining a second set of LIC parameters associated with the linear LIC model of the second block; and The second block is encoded based on the second set of LIC parameters.

41. The method according to claim 40, wherein The linear LIC model is a filter-based model, and the method further comprises: Identify the template sample of the second block; identifying, in the template of the reference block of the second block, a first reference template sample corresponding to the template sample of the second block; identifying a plurality of second reference template samples adjacent to the first reference template sample; and A set of filter coefficients is derived based on minimizing differences between template samples of the second block and corresponding predicted template samples obtained based on a plurality of second reference template samples.

42. The method of claim 40, wherein: The linear LIC model is a filter-based model comprising a set of filter coefficients, and the method further comprises: For a sample position in the second block, identifying a plurality of reference samples of a reference block associated with the second block; and Corresponding filter coefficients from the set of filter coefficients are applied to a plurality of neighboring samples to generate predicted samples of a second block.

43. The method of claim 40, wherein: The linear LIC model is a gradient-based model, and obtaining the second set of LIC parameters includes deriving the second set of LIC parameters based on gradients of reference samples of a reference block associated with the second block.

44. The method of claim 34, wherein: The polynomial LIC model is a gradient-based model, and the method further comprises: applying the set of LIC parameters to reference samples of a reference block and gradients of the reference samples to obtain a refined prediction of the block; and The block is encoded based on the refined prediction of the block.

45. A computer-readable medium comprising a medium comprising instructions for causing one or more processors to perform the method of any one of claims 23-44.

46. ​​Video data comprising information representing blocks encoded according to the method of any one of claims 34-44.

Images