Loop filter chroma balancing

Abstract

Systems, methods, and tools are disclosed for performing loop filter chroma balancing. For example, the reconstructed image can be filtered (e.g., post-filtered) using a neural network-based filter. The neural network-based filter can be used as a supplement or replacement for other filters. The neural network-based filter can be determined, for example, based on a neural network model. For example, the neural network model can be trained using one or more of a training dataset, a default quantization parameter (QP), a QP offset, etc. Refined frames and / or images can be determined based on the neural network-based filter.

Description

Cross-references to applications related to chromaticity balance of loop filters

[0001] This application claims the benefit of European Patent Application No. 23306654.7, filed on 2 October 2023, the contents of which are incorporated herein by reference in their entirety. Background Technology

[0002] 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

[0003] Systems, methods, and tools for performing chromaticity balancing using loop filters are disclosed. For example, a neural network-based filter can be used to filter (e.g., post-filter) a reconstructed image. The neural network-based filter can be used to complement or replace other filters. The neural network-based filter can be determined, for example, based on a neural network model. For example, one or more of the following can be used to train the neural network model: a training dataset, a default quantization parameter (QP), a QP offset, etc. Thinning frames and / or images can be determined based on the neural network-based filter.

[0004] A device (e.g., a video encoder, video decoder) can perform loop filter chroma balancing in association with a neural network-based filter. The device can obtain a reconstructed frame (e.g., an encoded reconstructed frame). Luminance and chroma components can be associated with the reconstructed frame. The device can decode the encoded reconstructed frame. Decoding the encoded reconstructed frame can generate the reconstructed frame and / or filter parameters associated with it. The device can determine the filter parameters associated with the reconstructed frame, for example, these filter parameters indicating the use of a neural network-based filter. The device can determine an input, for example, based on the reconstructed frame. The device can filter the input using a neural network model filter and balance information associated with the luminance and chroma components. The balance information can be obtained, for example, based on one or more of the following: attributes associated with the neural network-based filter; lookup tables; messages (e.g., information indicating the association with the neural network-based filter); etc. The device can determine a first QP offset (e.g., luminance QP offset) and a second QP offset (e.g., chroma QP offset) based, for example, on the balance information. Before filtering using a neural network-based filter, a first QP offset and / or a second QP offset can be applied to the input. The first QP offset can be the same as the second QP offset. Filtering the input can generate an output. A thinned frame can be determined and / or reconstructed based on the output. For example, a correction can be determined based on the output and a scaling factor. This correction can be added to the input. The thinned frame can be the correction added to the input. The device can include encoded thinned frames and / or indications of filter parameters associated with the neural network-based filter in the video data.

[0005] This device can determine (e.g., create or train) a neural network model. It can train the neural network model to generate a trained neural network model. A neural network-based filter can be determined based on the neural network model or the trained neural network model. The device can train the neural network model based on a default balance ratio (e.g., associated with luminance / chrominance). The device can further refine the trained neural network model, for example, based on the trained neural network model and a QP offset.

[0006] The systems, methods, and tools described herein may relate to decoders. In some examples, the systems, methods, and tools described herein may relate to encoders. In some examples, the systems, methods, and tools described herein may relate to signals (e.g., from an encoder and / or received by a decoder). A computer-readable medium may include instructions for causing one or more processors to perform the methods described herein. A computer program product may include instructions that, when executed by one or more processors, cause one or more processors to perform the methods described herein. Attached Figure Description

[0007] Figure 1A is a system diagram illustrating an example communication system that can implement one or more of the disclosed embodiments.

[0008] Figure 1B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that can be used within the communication system shown in Figure 1A according to an embodiment.

[0009] Figure 1C is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that can be used within the communication system shown in Figure 1A according to an embodiment.

[0010] Figure 1D is a system diagram illustrating yet another example RAN and yet another example CN that can be used within the communication system shown in Figure 1A according to an embodiment.

[0011] Figure 2 shows an example video encoder.

[0012] Figure 3 shows an example video decoder.

[0013] Figure 4 shows an example of a system in which various aspects and examples can be implemented.

[0014] Figure 5 illustrates an example loop filtering process in an example video codec.

[0015] Figure 6 illustrates an example CNN loop filter process in NNVC.

[0016] Figure 7 illustrates an example training process for learning a CNN.

[0017] Figure 8 shows an example of two-stage training.

[0018] Figure 9 shows an example dataset adaptation for balancing luminance / chrominance gain.

[0019] Figure 10 shows an example of a high-computation-point model used in a hybrid neural network video codec. Detailed Implementation

[0020] A more detailed understanding can be obtained from the following description, which is given by way of example in conjunction with the accompanying drawings.

[0021] Figure 1A is a system diagram illustrating an example communication system 100 that can implement one or more of the disclosed embodiments. The communication system 100 can be a multiple access system providing content such as voice, data, video, messaging, and broadcasting to multiple wireless users. The communication system 100 enables multiple wireless users to access such content through shared system resources including wireless broadband. For example, the communication system 100 can 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 Extended OFDM (ZT UW DTS-s OFDM), Unique Word OFDM (UW-OFDM), Resource Block Filtered OFDM, Filter Bank Multicarrier (FBMC), etc.

[0022] As shown in Figure 1A, 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, 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, and 102d may be any type of device configured to operate and / or communicate in a wireless environment. For example, WTRUs 102a, 102b, 102c, and 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), mobile stations, fixed or mobile subscriber units, subscription-based units, pagers, cellular phones, personal digital assistants (PDAs), smartphones, laptops, netbooks, personal computers, wireless sensors, hotspots or Mi-Fi devices, Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in the context of industrial and / or automated processing chains), consumer electronics devices, devices operating on commercial and / or industrial wireless networks, etc. Any of WTRUs 102a, 102b, 102c, and 102d may be interchangeably referred to as a UE.

[0023] The communication system 100 may also include base station 114a and / or base station 114b. Each of base stations 114a and 114b may be any type of device configured to wirelessly interface with at least one of WTRUs 102a, 102b, 102c, and 102d to facilitate access to one or more communication networks such as CN 106 / 115, Internet 110, and / or other networks 112. For example, base stations 114a and 114b may be base transceiver stations (BTS), node Bs, eNode Bs, master node Bs, master eNodeBs, gNBs, NR node Bs, site controllers, access points (APs), wireless routers, etc. Although base stations 114a and 114b are each depicted as a single element, it will be understood that base stations 114a and 114b may include any number of interconnected base stations and / or network elements.

[0024] 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 base station controllers (BSCs), radio network controllers (RNCs), relay nodes, etc. Base station 114a and / or base station 114b may be configured to transmit and / or receive radio 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 coverage for a specific geographic area that may be relatively fixed or may change over time. A cell may also be divided into cell sectors. For example, the cell associated with base station 114a may be divided into three sectors. Therefore, in one embodiment, base station 114a may include three transceivers, i.e., one for each sector of the cell. In embodiments, 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.

[0025] Base stations 114a and 114b can communicate with one or more of WTRUs 102a, 102b, 102c, and 102d via air interface 116, which can be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). Any suitable radio access technology (RAT) can be used to establish air interface 116.

[0026] More specifically, as described above, the communication system 100 can be a multiple access system and can employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, etc. For example, base station 114a in RAN 104 / 113 and WTRUs 102a, 102b, 102c can implement radio technologies, such as using Wideband CDMA (WCDMA) to establish Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA) for air interfaces 115 / 116 / 117. WCDMA can include communication protocols such as High-Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA can include High-Speed ​​Downlink (DL) Packet Access (HSDPA) and / or High-Speed ​​UL Packet Access (HSUPA).

[0027] In the embodiment, base station 114a and WTRUs 102a, 102b, 102c can implement radio technologies, such as using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro) to establish Evolved UMTS Terrestrial Radio Access (E-UTRA) for air interface 116.

[0028] In the embodiments, base station 114a and WTRUs 102a, 102b, 102c can implement radio technologies, such as using New Radio (NR) to establish NR radio access for air interface 116.

[0029] In the embodiments, base station 114a and WTRUs 102a, 102b, and 102c can implement multiple radio access technologies. For example, base station 114a and WTRUs 102a, 102b, and 102c can, for instance, use the dual connectivity (DC) principle to jointly implement LTE radio access and NR radio access. Therefore, the air interface used by WTRUs 102a, 102b, and 102c can 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).

[0030] In other embodiments, base station 114a and WTRUs 102a, 102b, 102c can implement radio technologies such as IEEE 802.11 (i.e., WiFi), IEEE 802.16 (i.e., WiMAX), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Provisional Standard 2000 (IS-2000), Provisional Standard 95 (IS-95), Provisional Standard 856 (IS-856), Global System for Mobile Communications (GSM), Enhanced Data Rate GSM Evolution (EDGE), GSMEDGE (GERAN), etc.

[0031] Base station 114b in Figure 1A can be, for example, a wireless router, master node B, master eNode B, or access point, and can utilize any suitable RAT to facilitate wireless connectivity in local areas such as commercial locations, homes, vehicles, campuses, industrial facilities, air corridors (e.g., for use by drones), roads, etc. In one embodiment, base station 114b and WTRUs 102c, 102d can implement radio technologies such as IEEE 802.11 to establish a wireless local area network (WLAN). In another embodiment, base station 114b and WTRUs 102c, 102d can implement radio technologies such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, base station 114b and WTRUs 102c, 102d can utilize cellular-based RATs (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-a, LTE-a Pro, NR, etc.) to establish picocells or femtocells. As shown in Figure 1A, base station 114b can be directly connected to the Internet 110. Therefore, base station 114b does not need to access Internet 110 via CN 106 / 115.

[0032] RAN 104 / 113 can communicate with CN 106 / 115, which can be any type of network configured to provide voice, data, application, and / or Voice over Internet Protocol (VoIP) services to one or more of WTRUs 102a, 102b, 102c, and 102d. Data can have different Quality of Service (QoS) requirements, such as different throughput requirements, latency requirements, fault tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. CN 106 / 115 can provide call control, billing services, location-based services, prepaid calling, internet connectivity, video distribution, etc., and / or perform advanced security functions such as user authentication. Although not shown in Figure 1A, it will be understood that RAN 104 / 113 and / or CN 106 / 115 can communicate directly or indirectly with other RANs using the same RAT as RAN 104 / 113 or a different RAT. For example, in addition to connecting to RAN 104 / 113, which may be using NR radio technology, CN 106 / 115 can also communicate with another RAN (not shown) using GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0033] CN 106 / 115 can also serve as a gateway for WTRU 102a, 102b, 102c, 102d to access PSTN 108, the Internet 110, and / or other networks 112. PSTN 108 may include a circuit-switched telephone network providing Common Old-Style Telephone Service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices using common communication protocols such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and / or Internet Protocol (IP) from the TCP / IP Internet Protocol suite. Network 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, network 112 may include another CN connected to one or more RANs, which may use the same RAT as RAN 104 / 113 or a different RAT.

[0034] Some or all of the WTRUs 102a, 102b, 102c, and 102d in the communication system 100 may include multi-mode capabilities (e.g., WTRUs 102a, 102b, 102c, and 102d may include multiple transceivers for communicating with different wireless networks via different wireless links). For example, the WTRU 102c shown in Figure 1A may be configured to communicate with a base station 114a that may employ cellular-based radio technology and with a base station 114b that may employ IEEE 802 radio technology.

[0035] Figure 1B is a system diagram illustrating an example WTRU 102. As shown in Figure 1B, WTRU 102 may include 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 peripheral devices 138, etc. It will be understood that, while remaining consistent with the embodiments, WTRU 102 may include any sub-combination of the foregoing elements.

[0036] Processor 118 may be a general-purpose processor, a special-purpose processor, a conventional processor, a digital signal processor (DSP), multiple 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, etc. Processor 118 may perform signal encoding, data processing, power control, input / output processing, and / or any other functions that enable WTRU 102 to operate in a wireless environment. Processor 118 may be coupled to transceiver 120, which may be coupled to transmitting / receiving element 122. Although Figure 1B depicts processor 118 and transceiver 120 as separate components, it will be understood that processor 118 and transceiver 120 may be integrated together in an electronic package or chip.

[0037] Transmitting / receiving element 122 can be configured to transmit signals to or receive signals from a base station (e.g., base station 114a) via air interface 116. For example, in one embodiment, transmitting / receiving element 122 can be an antenna configured to transmit and / or receive RF signals. In another embodiment, transmitting / receiving element 122 can be a transmitter / detector configured to transmit and / or receive, for example, IR, UV, or visible light signals. In yet another embodiment, transmitting / receiving element 122 can be configured to transmit and / or receive both RF signals and optical signals. It will be understood that transmitting / receiving element 122 can be configured to transmit and / or receive any combination of wireless signals.

[0038] Although the transmit / receive element 122 is depicted as a single element in FIG. 1B, 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 via the air interface 116.

[0039] Transceiver 120 can be configured to modulate signals transmitted by transmitting / receiving element 122 and demodulate signals received by transmitting / receiving element 122. As described above, WTRU 102 can have multi-mode capability. Therefore, transceiver 120 can include multiple transceivers for enabling WTRU 102 to communicate via various RATs (e.g., such as NR and IEEE 802.11).

[0040] The processor 118 of WTRU 102 can be coupled to and receive user input data from: a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) unit or an organic light-emitting diode (OLED) display unit). The processor 118 can also output user data to the speaker / microphone 124, keypad 126, and / or display / touchpad 128. Additionally, the processor 118 can access information and store data from any suitable type of memory, such as non-removable memory 130 and / or removable memory 132. 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. Removable memory 132 may include a subscriber identity module (SIM) card, memory stick, secure digital storage (SD) card, etc. In other embodiments, the processor 118 can access information and store data from memory not actually located on WTRU 102, such as on a server or home computer (not shown).

[0041] The processor 118 may receive power from the power supply 134 and may be configured to distribute power to other components in the WTRU 102 and / or control power to those other components. The power supply 134 may be any suitable device for powering the WTRU 102. For example, the power supply 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, etc.

[0042] The processor 118 may also be coupled to a GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) about the current location of the WTRU 102. In addition to or instead of information from the GPS chipset 136, the WTRU 102 may receive location information from base stations (e.g., base stations 114a, 114b) via 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 understood that, while remaining consistent with the embodiments, the WTRU 102 may acquire location information using any suitable location determination method.

[0043] The processor 118 can also be connected to other peripheral devices 138, which may include one or more software and / or hardware modules that provide additional features, functions, and / or wired or wireless connectivity. For example, peripheral devices 138 may include accelerometers, electronic compasses, satellite transceivers, digital cameras (for photos and / or videos), Universal Serial Bus (USB) ports, vibration devices, television transceivers, hands-free headsets, Bluetooth® modules, 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, etc. Peripheral devices 138 may include one or more sensors, which may be one or more of the following: gyroscopes, accelerometers, Hall effect sensors, magnetometers, orientation sensors, proximity sensors, temperature sensors, time sensors; geolocation sensors; altimeters, light sensors, touch sensors, magnetometers, barometers, gesture sensors, biometric sensors, and / or humidity sensors.

[0044] WTRU 102 may include a full-duplex radio, wherein some or all of the transmission and reception of signals (e.g., associated with a specific subframe of 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 to reduce and / or substantially eliminate self-interference via hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In embodiments, WTRU 102 may include a half-duplex radio, wherein some or all of the transmission and reception of signals (e.g., associated with a specific subframe of both UL (e.g., for transmission) or downlink (e.g., for reception)) may be concurrent and / or simultaneous.

[0045] Figure 1C is a system diagram illustrating RAN 104 and CN 106 according to an embodiment. As described above, RAN 104 can employ E-UTRA radio technology to communicate with WTRUs 102a, 102b, and 102c via air interface 116. RAN 104 can also communicate with CN 106.

[0046] RAN 104 may include eNode-Bs 160a, 160b, and 160c, but it will be understood that RAN 104 may include any number of eNode-Bs while remaining consistent with the embodiments. eNode-Bs 160a, 160b, and 160c may each include one or more transceivers for communicating with WTRUs 102a, 102b, and 102c via air interface 116. In one embodiment, eNode-Bs 160a, 160b, and 160c may implement MIMO technology. Therefore, for example, eNode-B 160a may use multiple antennas to transmit radio signals to and / or receive radio signals from WTRU 102a.

[0047] Each of the eNode-B 160a, 160b, and 160c can be associated with a specific cell (not shown) and can be configured to handle radio resource management decisions, handover decisions, user scheduling in UL and / or DL, etc. As shown in Figure 1C, the eNode-B 160a, 160b, and 160c can communicate with each other via the X2 interface.

[0048] The CN 106 shown in Figure 1C 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 described 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.

[0049] The MME 162 can connect to each of the eNode-Bs 162a, 162b, and 162c in RAN 104 via the S1 interface and can act as a control node. For example, the MME 162 can be responsible for authenticating users of WTRUs 102a, 102b, and 102c, activating / deactivating bearers, selecting a specific serving gateway during the initial attachment of WTRUs 102a, 102b, and 102c, etc. The MME 162 can provide control plane functions for handover between RAN 104 and other RANs (not shown) employing other radio technologies such as GSM and / or WCDMA.

[0050] The SGW 164 can connect to each of the eNode Bs 160a, 160b, and 160c in RAN 104 via the S1 interface. The SGW 164 can typically route and forward user data packets to or from WTRUs 102a, 102b, and 102c. The SGW 164 can perform other functions such as anchoring the user plane during eNode-B handover, triggering paging when DL data is available to WTRUs 102a, 102b, and 102c, and managing and storing the context of WTRUs 102a, 102b, and 102c.

[0051] SGW 164 can be connected to PGW 166, which can provide WTRU 102a, 102b, 102c with access to packet-switched networks (such as Internet 110) to facilitate communication between WTRU 102a, 102b, 102c and IP-enabled devices.

[0052] CN 106 can facilitate communication with other networks. For example, CN 106 can provide WTRUs 102a, 102b, and 102c with access to circuit-switched networks (such as PSTN 108) to facilitate communication between WTRUs 102a, 102b, and 102c and traditional terrestrial line communication equipment. For example, CN 106 may include an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that serves as an interface between CN 106 and PSTN 108, or can communicate with it. Additionally, CN 106 can provide WTRUs 102a, 102b, and 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers.

[0053] Although the WTRU is depicted as a wireless terminal in Figures 1A through 1D, it is envisioned that, in some representative embodiments, such a terminal may (e.g., temporarily or permanently) use a wired communication interface with a communication network.

[0054] In a representative embodiment, the other network 112 may be a WLAN.

[0055] A WLAN in Infrastructure Basic Services 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 access a distribution system (DS) or another type of wired / wireless network that loads traffic into and / or loads traffic out of the BSS, or have an interface to it. Traffic originating outside the BSS destined for a STA can be delivered to the AP via it. Traffic from a STA destined for a destination outside the BSS can be sent to the AP for delivery to the appropriate destination. Traffic between STAs within the BSS can be sent via the AP, for example, where a source STA can send traffic to the AP, and the AP can deliver the traffic to the destination STA. Traffic between STAs within the BSS can be considered and / or referred to as point-to-point traffic. Point-to-point traffic can be sent between a source STA and a destination STA using a direct link setup (DLS) (e.g., directly between them). In some representative embodiments, the DLS may use 802.11e DLS or 802.11z Tunneled DLS (TDLS). A WLAN using the Standalone BSS (IBSS) mode may not have an access point (AP), and STAs within the IBSS or using the IBSS (e.g., all STAs) can communicate directly with each other. The IBSS communication mode may sometimes be referred to as a "self-organizing" communication mode in this document.

[0056] When operating in 802.11ac infrastructure mode or a similar mode, the AP can transmit beacons on a fixed channel, such as the primary channel. The primary channel can be of fixed width (e.g., a 20 MHz bandwidth) or dynamically set via signaling. The primary channel can be the operating channel of the BSS and can be used by the STA to establish a connection with the AP. In some representative embodiments, Carrier Sense Multiple Access - Collision Avoidance (CSMA / CA) can be implemented, for example, in an 802.11 system. For CSMA / CA, each STA (e.g., every STA), including the AP, can sense the primary channel. If a particular STA senses / detects that the primary signal is busy and / or determines that the primary signal is busy, that STA can back off. In a given BSS, at any given time, only one STA (e.g., only one station) can transmit.

[0057] High-throughput (HT) STAs can communicate using a 40 MHz wide channel, for example, by combining a primary 20 MHz channel with adjacent or non-adjacent 20 MHz channels.

[0058] Very High Throughput (VHT) STAs can support channels with widths of 20 MHz, 40 MHz, 80 MHz, and / or 160 MHz. 40 MHz and / or 80 MHz channels can be formed by combining consecutive 20 MHz channels. A 160 MHz channel can be formed by combining eight consecutive 20 MHz channels, or by combining two non-consecutive 80 MHz channels, which can be referred to as an 80+80 configuration. In the 80+80 configuration, after channel coding, data can be passed through a fragment parser, which splits the data into two streams. Inverse Fast Fourier Transform (IFFT) processing and time-domain processing can be performed on each stream separately. The streams can be mapped onto the two 80 MHz channels, and the data can be transmitted by the transmitting STA. At the receiver of the receiving STA, the above operations of the 80+80 configuration can be reversed, and the combined data can be sent to the Media Access Control (MAC).

[0059] 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 compared to those used in 802.11n and 802.11ac. 802.11af supports 5 MHz, 10 MHz, and 20 MHz bandwidths in the TV Blank (TVWS) spectrum, and 802.11ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to representative embodiments, 802.11ah can support instrument-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 (e.g., only support) certain and / or limited bandwidths. MTC devices may include batteries with a battery life exceeding a threshold (e.g., to maintain a very long battery life).

[0060] WLAN systems that can support multiple channels and channel bandwidths (such as 802.11n, 802.11ac, 802.11af, and 802.11ah) include a channel that can be designated as the primary channel. 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 the STAs operating in the BSS that support the minimum bandwidth operating mode. In the 802.11ah example, for STAs that support (e.g., only support) the 1 MHz mode (e.g., MTC type devices), 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 Sense and / or Network Assignment Vector (NAV) settings can depend on the status of the primary channel. If the primary channel is busy, for example, due to STAs (which only support the 1 MHz operating mode) transmitting to the AP, the entire available band may be considered busy even if most of the band remains idle and potentially available.

[0061] In the United States, the available frequency band for 802.11ah is 902 MHz to 928 MHz. In South Korea, the available frequency band is 917.5 MHz to 923.5 MHz. In Japan, the available frequency band is 916.5 MHz to 927.5 MHz. The total available bandwidth for 802.11ah is 6 MHz to 26 MHz, depending on the country code.

[0062] Figure 1D is a system diagram illustrating RAN 113 and CN 115 according to an embodiment. As described above, RAN 113 may employ NR radio technology to communicate with WTRUs 102a, 102b, and 102c via air interface 116. RAN 113 may also communicate with CN 115.

[0063] RAN 113 may include gNBs 180a, 180b, and 180c, but it will be understood that RAN 113 may include any number of gNBs while remaining consistent with the embodiments. gNBs 180a, 180b, and 180c may each include one or more transceivers for communicating with WTRUs 102a, 102b, and 102c via air interface 116. In one embodiment, gNBs 180a, 180b, and 180c may implement MIMO technology. For example, gNBs 180a and 180b may utilize beamforming to transmit signals to and / or receive signals from gNBs 180a, 180b, and 180c. Thus, for example, gNB 180a may use multiple antennas to transmit radio signals to and / or receive radio signals from WTRU 102a. In embodiments, gNBs 180a, 180b, and 180c may implement carrier aggregation technology. For example, gNB 180a can transmit multiple component carriers to WTRU 102a (not shown). A subset of these component carriers may be located on unlicensed spectrum, while the remaining component carriers may be located on licensed spectrum. In embodiments, gNBs 180a, 180b, and 180c can implement Coordinated Multipoint (CoMP) technology. For example, WTRU 102a can receive coordinated transmissions from gNBs 180a and 180b (and / or gNB 180c).

[0064] WTRUs 102a, 102b, and 102c can communicate with gNBs 180a, 180b, and 180c using transmissions associated with scalable parameter sets. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing can be varied for different transmissions, different cells, and / or different portions of the radio transmission spectrum. WTRUs 102a, 102b, and 102c can communicate with gNBs 180a, 180b, and 180c using subframes of various or scalable lengths or transmission time intervals (TTIs) (e.g., containing different numbers of OFDM symbols and / or absolute times of varying durations).

[0065] gNBs 180a, 180b, and 180c can be configured to communicate with WTRUs 102a, 102b, and 102c in standalone and / or non-standalone configurations. In standalone configuration, WTRUs 102a, 102b, and 102c can communicate with gNBs 180a, 180b, and 180c without accessing other RANs (e.g., eNode-Bs 160a, 160b, and 160c). In standalone configuration, WTRUs 102a, 102b, and 102c can use one or more of gNBs 180a, 180b, and 180c as mobile anchors. In standalone configuration, WTRUs 102a, 102b, and 102c can communicate with gNBs 180a, 180b, and 180c using signals in unlicensed frequency bands. In a non-standalone configuration, WTRUs 102a, 102b, and 102c can communicate / connect with gNBs 180a, 180b, and 180c while also communicating / connecting with another RAN (such as eNode-Bs 160a, 160b, and 160c). For example, WTRUs 102a, 102b, and 102c can implement DC principles to communicate substantially simultaneously with one or more gNBs 180a, 180b, and 180c and one or more eNode-Bs 160a, 160b, and 160c. In a non-standalone configuration, eNode-Bs 160a, 160b, and 160c can act as mobile anchors for WTRUs 102a, 102b, and 102c, and gNBs 180a, 180b, and 180c can provide additional coverage and / or throughput to serve WTRUs 102a, 102b, and 102c.

[0066] Each of gNBs 180a, 180b, and 180c can be associated with a specific cell (not shown) and can be configured to handle radio resource management decisions, handover decisions, user scheduling in UL and / or DL, support for network slicing, dual connectivity, interoperability between NR and E-UTRA, routing of user plane data to User Plane Functions (UPF) 184a and 184b, routing of control plane information to Access and Mobility Management Functions (AMF) 182a and 182b, etc. As shown in Figure 1D, gNBs 180a, 180b, and 180c can communicate with each other via the Xn interface.

[0067] The CN 115 shown in Figure 1D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. While each of the foregoing elements is described 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.

[0068] AMF 182a and 182b can connect to one or more of the gNBs 180a, 180b, and 180c in RAN 113 via the N2 interface and can act as control nodes. For example, AMF 182a and 182b can be responsible for authenticating users of WTRU 102a, 102b, and 102c, supporting network slicing (e.g., handling different PDU sessions with different requirements), selecting specific SMFs 183a and 183b, managing registration areas, terminating NAS signaling, mobility management, etc. AMF 182a and 182b can use network slicing to customize CN support for WTRU 102a, 102b, and 102c based on the service types being used by WTRU 102a, 102b, and 102c. For example, different network slices can 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, and services for Machine Type Communication (MTC) access. AMF 162 can provide control plane functions for handover between RAN 113 and other RANs (not shown) that employ other radio technologies (such as LTE, LTE-A, LTE-A Pro) and / or non-3GPP access technologies (such as WiFi).

[0069] SMFs 183a and 183b can connect to AMFs 182a and 182b in CN 115 via the N11 interface. SMFs 183a and 183b can also connect to UPFs 184a and 184b in CN 115 via the N4 interface. SMFs 183a and 183b can select and control UPFs 184a and 184b, and configure traffic routing through UPFs 184a and 184b. SMFs 183a and 183b can perform other functions, such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, and providing downlink data notifications. PDU session types can be IP-based, non-IP-based, or Ethernet-based.

[0070] UPF 184a and 184b can connect via the N3 interface to one or more of the gNBs 180a, 180b, and 180c in RAN 113. These gNBs can provide WTRU 102a, 102b, and 102c with access to packet-switched networks (such as the Internet 110) to facilitate communication between WTRU 102a, 102b, and 102c and IP-enabled devices. UPF 184 and 184b can perform other functions such as routing and forwarding packets, enforcing user plane policies, supporting multihomed PDU sessions, handling user plane QoS, buffering downlink packets, and providing mobility anchoring.

[0071] CN 115 can facilitate communication with other networks. For example, CN 115 may include or be able to communicate with an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that serves as an interface between CN 115 and PSTN 108. Additionally, CN 115 can provide WTRUs 102a, 102b, and 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, WTRUs 102a, 102b, and 102c can be connected to DN 185a and 185b via UPF 184a and 184b through the N3 interface to UPF 184a and 184b and the N6 interface between UPF 184a and 184b and local data networks (DNs) 185a and 185b.

[0072] Based on the corresponding descriptions in Figures 1A-1D, one or more emulation devices (not shown) can perform one or more or all of the functions described herein with respect to one or more of the following: WTRU 102a-102d, base stations 114a-114b, eNode-B 160a-160c, MME 162, SGW 164, PGW 166, gNB 180a-180c, AMF 182a-182b, UPF 184a-184b, SMF 183a-183b, DN 185a-185b, and / or any other devices described herein. An emulation device can be one or more devices configured to emulate one or more or all of the functions described herein. For example, an emulation device can be used to test other devices and / or simulate network and / or WTRU functions.

[0073] Simulation devices can be designed to perform one or more tests on other devices in laboratory and / or carrier network environments. For example, one or more simulation devices may perform one or more functions when fully or partially implemented and / or deployed as part of a wired and / or wireless communication network to test other devices within the communication network. One or more simulation devices may perform one or more functions when temporarily implemented / deployed as part of a wired and / or wireless communication network. Simulation devices may be directly coupled to another device for testing purposes and / or may use over-the-air wireless communication to perform tests.

[0074] One or more simulation devices may perform one or more functions without being implemented / deployed as part of a wired and / or wireless communication network. For example, a simulation device may be used to test scenarios in a laboratory and / or an undeployed (e.g., tested) wired and / or wireless communication network to enable testing of one or more components. One or more simulation devices may be test equipment. Simulation devices may transmit and / or receive data using direct RF connections and / or wireless communication via an RF circuit system (e.g., which may include one or more antennas).

[0075] This application describes various aspects, including tools, features, embodiments, models, methods, etc. Many of these aspects are described in a specific manner and, at least for the purpose of illustrating the individual features, are generally described in a way that may sound limiting. However, this is for the purpose of clarity of description and does not limit the application or scope of these aspects. In fact, all the different aspects can be combined and interchanged to provide other aspects. Furthermore, these aspects can also be combined and interchanged with aspects described in previous documents.

[0076] The aspects described and contemplated in this application can be implemented in many different forms. Figures 5 through 10 described herein provide some examples, but other examples are contemplated. The discussion of Figures 5 through 10 does not limit the breadth of implementations. At least one aspect generally relates to video encoding and decoding, and at least one other aspect generally relates to transmitting the generated or encoded bitstream. These and other aspects can be implemented as methods, apparatus, computer-readable storage media having instructions thereon stored thereon for encoding or decoding video data according to any of the methods, and / or computer-readable storage media having bitstreams generated according to any of the methods stored thereon.

[0077] In this application, the terms “reconstruction” and “decoding” are used interchangeably, the terms “pixel” and “sample” are used interchangeably, and the terms “image”, “picture” and “frame” are used interchangeably.

[0078] This document describes various methods, each of which includes one or more steps or actions to implement the described method. Unless the correct operation of the method requires a specific order of steps or actions, the order and / or use of specific steps and / or actions can be modified or combined. Additionally, in various examples, terms such as "first," "second," etc., may be used to modify elements, components, steps, operations, etc., such as, for example, "first decoding" and "second decoding." Unless specifically required, the use of such terms does not imply a sequence of operations. Therefore, in this example, the first decoding does not need to be performed before the second decoding, but can occur, for example, before, during, or in a time period overlapping with the second decoding.

[0079] The various methods and other aspects described in this application can be used to modify modules of the video encoder 200 and decoder 300 shown in Figures 2 and 3, such as the decoding module. Furthermore, the subject matter disclosed herein can be applied to, for example, any type, format, or version of video coding, whether described in standards or recommendations (whether pre-existing or future-developed) and any extensions to such standards and recommendations. Unless otherwise stated or technically excluded, these aspects described in this application may be used alone or in combination.

[0080] Various numerical values ​​are used in the examples described in this application. These and other specific values ​​are for illustrative purposes only, and the aspects described are not limited to these specific values.

[0081] Figure 2 is a diagram illustrating an example video encoder. Variations of the example encoder 200 are envisioned, but for clarity, encoder 200 is described below without describing all anticipated variations.

[0082] Before being encoded, the video sequence may undergo pre-coding processing 201, such as applying color transformations to the input color image (e.g., a conversion from RGB 4:4:4 to YCbCr 4:2:0), or performing remapping of the input image components to obtain a signal distribution that is more resilient to compression (e.g., using histogram equalization with one of the color components). Metadata may be associated with the pre-processing and appended to the bitstream.

[0083] In encoder 200, the image is encoded by encoder elements, as described below. The image to be encoded is partitioned (202) and processed in units such as coding units (CUs). Each unit is encoded using, for example, an intra-frame or inter-frame mode. When a unit is encoded in intra-frame mode, intra-frame prediction (260) is performed. In inter-frame mode, motion estimation (275) and compensation (270) are performed. The encoder determines (205) which mode, intra-frame or inter-frame, to use to encode the unit, and indicates the intra-frame / inter-frame decision by, for example, a prediction mode flag. For example, the prediction residual is calculated by subtracting (210) the prediction block from the original image block.

[0084] The predicted residual is then transformed (225) and quantized (230). The quantized transform coefficients, motion vectors, and other syntactic 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., directly encode the residual without applying either the transform or quantization process.

[0085] 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 decoded prediction residual and the prediction block are combined (255) to reconstruct the image block. An in-loop filter (265) is applied to the reconstructed image to perform, for example, deblocking / SAO (Sample Adaptive Shift) filtering, thereby reducing coding artifacts. The filtered image is stored in a reference image buffer (280).

[0086] Figure 3 is a diagram illustrating an example video decoder. In the example decoder 300, the bitstream is decoded by decoder elements as described below. The video decoder 300 typically performs a decoding process that is the reverse of the encoding process described in Figure 2. The encoder 200 typically also performs video decoding as part of the encoded video data.

[0087] Specifically, the decoder's input includes a video bitstream, which can be generated by the video encoder 200. First, entropy decoding (330) is performed on the bitstream to obtain transform coefficients, motion vectors, and other encoded information. Image partitioning information indicates how the image is partitioned. Therefore, the decoder can partition (335) the image based on the decoded image partitioning information. The transform coefficients are dequantized (340) and inverse transformed (350) to decode the prediction residuals. The decoded prediction residuals and prediction blocks are combined (355) to reconstruct the image blocks. Prediction blocks (370) can be obtained from intra-frame prediction (360) or motion-compensated prediction (i.e., inter-frame prediction) (375). An in-loop filter (365) is applied to the reconstructed image. The filtered image is stored in a reference image buffer (380).

[0088] The decoded image can also undergo post-decoding processing (385), such as inverse color transformation (e.g., conversion from YCbCr 4:2:0 to RGB 4:4:4) or inverse remapping, which is the inverse of the remapping process performed in the pre-encoding process (201). Post-decoding processing can use metadata derived in the pre-encoding process and signaled in the bitstream. In the example, the decoded image (e.g., after applying an in-loop filter (365) and / or, in the case of post-decoding processing, after post-decoding processing (385)) can be sent to a display device for presentation to the user.

[0089] Figure 4 is a diagram illustrating an example of a system in which the various aspects and examples described herein can be implemented. System 400 may be embodied as a device including the various components described below and configured to perform one or more aspects described in this document. Examples of such devices include, but are not limited to, various electronic devices such as personal computers, laptop computers, smartphones, tablet computers, digital multimedia set-top boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. Elements of system 400 may be embodied 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 elements and encoder / decoder elements of system 400 are distributed across multiple ICs and / or discrete components. In various examples, system 400 is communicatively coupled to one or more other systems or other electronic devices via, for example, a communication bus or through dedicated input and / or output ports. In various examples, system 400 is configured to implement one or more aspects described in this document.

[0090] System 400 includes at least one processor 410 configured to execute instructions loaded thereon to implement various aspects described herein, such as those described. Processor 410 may include embedded memory, input / output interfaces, and various other circuitry known in the art. System 400 includes at least one memory 420 (e.g., a volatile memory device and / or a non-volatile memory device). System 400 includes a storage device 440 that 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, disk drives, and / or optical disk drives. As a non-limiting example, storage device 440 may include internal storage devices, attached storage devices (including removable and non-removable storage devices), and / or network-accessible storage devices.

[0091] System 400 includes an encoder / decoder module 430 configured to, for example, process data to provide encoded or decoded video, and the encoder / decoder module 430 may include its own processor and memory. The encoder / decoder module 430 represents a module that can be included in a device to perform encoding and / or decoding functions. It is well known that a device can include one or both encoding and decoding modules. Alternatively, the encoder / decoder module 430 may be implemented as a separate element of system 400, or it may be incorporated within processor 410 as a combination of hardware and software known to those skilled in the art.

[0092] Program code to be loaded onto processor 410 or encoder / decoder 430 to execute the various aspects described in this document may be stored in storage device 440 and subsequently loaded onto memory 420 for execution by processor 410. Depending on various examples, one or more of processor 410, memory 420, storage device 440, and encoder / decoder module 430 may store one or more of various items during the execution of the processes described in this document. Such stored items 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 equations, formulas, operations, and operational logic processing.

[0093] In some examples, the memory within processor 410 and / or encoder / decoder module 430 is used to store instructions and provide working memory for processing required during encoding or decoding. However, in other examples, external memory (e.g., the processing device could be processor 410 or encoder / decoder module 430) is used for one or more of these functions. External memory could be memory 420 and / or storage device 440, such as volatile memory and / or non-volatile flash memory. In several examples, external non-volatile flash memory is used to store, for example, the operating system of a television. In at least one example, fast external volatile memory such as RAM is used as working memory for video encoding and decoding operations.

[0094] Input to the components of system 400 can be provided through various input devices, as indicated in box 445. Such input devices include, but are not limited to, (i) a radio frequency (RF) section that receives, for example, RF signals transmitted over the air 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. Other examples not shown in Figure 4 include composite video.

[0095] In various examples, the input device of block 445 has corresponding input processing elements known in the art. For example, the RF section may be associated with elements suitable for: (i) selecting a desired frequency (also known as selecting a signal, or limiting the signal band to a band), (ii) down-converting the selected signal, (iii) further band-limiting to a narrower band to select (e.g.,) a signal band that may be referred to as a channel in some examples), (iv) demodulating the down-converted and band-limited signal, (v) performing error correction, and / or (vi) demultiplexing to select the desired data packet stream. The RF section of various examples includes one or more elements performing these functions, such as frequency selectors, signal selectors, band limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF section may include tuners performing various of these functions, including, for example, down-converting a received signal to a lower frequency (e.g., intermediate frequency or near-baseband frequency) or baseband. In one set-top box example, the RF section and its associated input processing elements receive RF signals transmitted via a wired (e.g., cable) medium and perform frequency selection by filtering, down-converting, and filtering again to the desired frequency band. Various examples rearrange the above (and other) components, remove some of them, and / or add other components that perform similar or different functions. Adding components may include inserting components between existing components, such as inserting amplifiers and analog-to-digital converters. In various examples, the RF section includes an antenna.

[0096] USB and / or HDMI terminals may include corresponding interface processors for connecting system 400 to other electronic devices via USB and / or HDMI connections. It should be understood that aspects of input processing (e.g., Reed-Solomon error correction) may be implemented, for example, within a separate input processing IC or within processor 410 as needed. Similarly, aspects of USB or HDMI interface processing may be implemented, as needed, within a separate interface IC or within processor 410. The demodulated, error-corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 410 and an encoder / decoder 430 operating in conjunction with memory and storage elements, to process the data stream as needed for presentation on an output device.

[0097] Various components of system 400 can be housed within an integrated housing. Within the integrated housing, the various components can be interconnected and transmit data between them using suitable connection devices 425 (e.g., internal buses known in the art, including inter-IC (I2C) buses, wiring, and printed circuit boards).

[0098] System 400 includes a communication interface 450 that enables communication with other devices via a communication channel 460. The communication interface 450 may include, but is not limited to, a transceiver configured to send and receive data via the communication channel 460. The communication interface 450 may include, but is not limited to, a modem or network interface card (NIC), and the communication channel 460 may be implemented, for example, within a wired and / or wireless medium.

[0099] In various examples, data is streamed to or otherwise provided to system 400 using a wireless network, such as a Wi-Fi network, for example, IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers). In these examples, the Wi-Fi signal is received via a communication channel 460 and a communication interface 450 adapted for Wi-Fi communication. The communication channel 460 in these examples 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 examples use a set-top box to provide streaming data to system 400, delivering data via an HDMI connection to input block 445. Still other examples use an RF connection to input block 445 to provide streaming data to system 400. As indicated above, various examples provide data in a non-streaming manner. Additionally, various examples use wireless networks other than Wi-Fi, such as cellular networks or Bluetooth® networks.

[0100] System 400 can provide output signals to various output devices, including display 475, speaker 485, and other peripheral devices 495. Various examples of display 475 include one or more of, for example, touchscreen displays, organic light-emitting diode (OLED) displays, curved displays, and / or foldable displays. Display 475 can be used in televisions, tablets, laptops, mobile phones, or other devices. Display 475 can also be integrated with other components (e.g., in a smartphone) or standalone (e.g., an external monitor for a laptop computer). In various examples, other peripheral devices 495 include one or more of standalone digital video discs (or digital multifunction discs) (DVDs, for both terms), disc players, stereo systems, and / or lighting systems. Various examples use one or more peripheral devices 495 that provide functionality based on the output of system 400. For example, a disc player performs the function of playing the output of system 400.

[0101] In various examples, signaling (such as AV.Link, Consumer Electronics Control (CEC), or other communication protocols that enable device-to-device control with or without user intervention) is used to transmit control signals between system 400 and display 475, speaker 485, or other peripheral devices 495. Output devices may be communicatively coupled to system 400 via dedicated connections through corresponding interfaces 470, 480, and 490. Alternatively, output devices may be connected to system 400 via communication interface 450 using communication channel 460. Display 475 and speaker 485 may be integrated into a single unit along with other components of system 400 in electronic devices such as televisions. In various examples, display interface 470 includes a display driver, such as a timing controller (TCon) chip.

[0102] For example, if the RF input section 445 is part of a separate set-top box, the display 475 and speaker 485 can alternatively be separate from one or more of the other components. In various examples where the display 475 and speaker 485 are external components, the output signal can be provided via a dedicated output connection, including, for example, an HDMI port, a USB port, or a COMP output.

[0103] The example can be executed by processor 410 or by computer software implemented by hardware or a combination of hardware and software. As a non-limiting example, the example can be implemented by one or more integrated circuits. As a non-limiting example, memory 420 can be of any type suitable for the technical environment and can be implemented using any suitable 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, processor 410 can be of any type suitable for the technical environment and can encompass one or more microprocessors, general-purpose computers, special-purpose computers, and processors based on multi-core architectures.

[0104] Various implementations involve decoding. As used herein, "decoding" can encompass all or part of a process performed, for example, on a received encoded sequence to produce a final output suitable for display. In various examples, such a process includes one or more processes typically performed by a decoder, such as entropy decoding, inverse quantization, inverse transform, and differential decoding. In various examples, such a process also includes, or alternatively includes, processes performed by a decoder of the various implementations described herein, such as obtaining a reconstructed frame, determining an input, filtering the input using a neural network model filter, determining a thinned frame, including the encoded thinned frame in the video data, including indices in the video data indicating filter parameters associated with the neural network model filter, etc.

[0105] As further examples, in one example, "decoding" refers only to entropy decoding; in another example, "decoding" refers only to differential decoding; and in yet another example, "decoding" refers to a combination of entropy decoding and differential decoding. Based on the specific context of the description, it will be clear whether the phrase "decoding process" is intended to specifically refer to a subset of operations or to refer to a broader decoding process, and it is believed that those skilled in the art will readily understand this.

[0106] Various implementations involve encoding. Similar to the discussion of "decoding" above, "encoding" as used in this application can include, for example, all or part of the processing performed on an input video sequence to produce an encoded bitstream. In various examples, such processes include one or more processes typically performed by an encoder, such as partitioning, differential coding, transform, quantization, and entropy coding. In various examples, such processes also include, or alternatively include, processes performed by an encoder of the various implementations described in this application, such as obtaining an encoded reconstructed frame, decoding the encoded reconstructed frame, determining the reconstructed frame, determining filter parameters associated with the reconstructed frame, determining a filter to use a neural network model, determining the input, filtering the input using a neural network model filter, reconstructing a refined frame based on the output, etc.

[0107] As further examples, in one example, "encoding" refers only to entropy encoding; in another example, "encoding" refers only to differential encoding; and in yet another example, "encoding" refers to a combination of entropy encoding and differential encoding. Based on the specific context of the description, it will be clear whether the phrase "encoding process" is intended to specifically refer to a subset of operations or to refer to a broader encoding process, and it is believed that those skilled in the art will readily understand this.

[0108] It should be noted that the syntactic elements used in this paper (e.g., the encoding syntax for neural network filter indications, etc.) are descriptive terms. Therefore, they do not preclude the use of other syntactic element names.

[0109] When a diagram is presented as a flowchart, it should be understood that it also provides a block diagram of the corresponding apparatus. Similarly, when a diagram is presented as a block diagram, it should be understood that it also provides a flowchart of the corresponding method / process.

[0110] The implementations and aspects described herein can be implemented, for example, in methods or processes, apparatuses, software programs, data streams, or signals. Even if discussed only in the context of a single implementation (e.g., discussed only as a method), the implementation of the features in question can also be implemented in other forms (e.g., apparatuses or programs). Apparatuses can be implemented, for example, with appropriate hardware, software, and firmware. Methods can be implemented, for example, in a processor, which generally refers to a processing device, including, for example, a computer, microprocessor, integrated circuit, or programmable logic device. Processors also include communication devices, such as computers, cellular phones, portable / personal digital assistants (“PDAs”), and other devices that facilitate information communication between end users.

[0111] The reference to “an example” or “an example” or “an implementation” or “an implementation”, and their variations, means that the specific features, structures, characteristics, etc., described in connection with the example are included in at least one example. Therefore, the phrases “in an example” or “in the example” or “in an implementation” or “in the implementation”, and any other variations, appearing throughout this application, do not necessarily refer to the same example.

[0112] Additionally, this application may relate to "determining" various types of information. Determining information may include one or more of the following: for example, estimated information, calculated information, predicted information, or information retrieved from memory. Obtaining may include receiving, retrieving, constructing, generating, and / or determining.

[0113] Furthermore, this application may involve "accessing" various types of information. Accessing information may include one or more of the following: for example, receiving information, retrieving information (e.g., retrieving from memory), storing information, moving information, copying information, calculating information, determining information, predicting information, or estimating information.

[0114] Additionally, this application may relate to "receiving" various types of information. Like "access," the intent to receive is a broad term. Receiving information may include one or more of the following: for example, accessing information or retrieving information (e.g., retrieving from memory). Furthermore, "receiving" is generally referred to in one or more ways during operation, such as storing information, processing information, transmitting information, moving information, copying information, erasing information, calculating information, determining information, predicting information, or estimating information.

[0115] It should be understood that, for example, in the cases of “A / B,” “A and / or B,” and “at least one of A and B,” the use of any of the following “ / ,” “and / or,” and “at least one” is intended to cover selecting only the first listed option (A), or only the second listed option (B), or selecting both options (A and B). As yet another example, in the cases of “A, B, and / or C” and “at least one of A, B, and C,” this wording is intended to include 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 and second listed options (A and B), or only the first and third listed options (A and C), or only the second and third listed options (B and C), or selecting all three options (A, B, and C). As will be apparent to those skilled in the art and related fields, this can be extended to a large number of listed items.

[0116] Furthermore, as used herein, the word “signal” specifically refers to instructing the corresponding decoder to do something. Encoder signals may include, for example, neural network filter parameters, neural network filters, QP information, QP offsets, etc. In this way, in the examples, the same parameters are used on both the encoder and decoder sides. Thus, for example, the encoder can send (explicit signaling) specific parameters to the decoder so that the decoder can use the same specific parameters. Conversely, if the decoder already has specific parameters as well as other parameters, signaling can be used without sending (implicit signaling) to simply allow the decoder to know and select specific parameters. Bit savings are achieved in various examples by avoiding the transmission of any actual functionality. It should be understood that signaling can be implemented in many ways. For example, in various examples, one or more syntactic elements, flags, etc., are used to send information to the corresponding decoder. Although the verb form of the word “signal” was mentioned above, the word “signal” can also be used as a noun in this article.

[0117] As will be apparent to those skilled in the art, implementations can produce various signals formatted to carry, for example, signals that can be stored or transmitted. Information may include, for example, instructions for performing a method, or data generated by one of the described implementations. For example, a signal may be formatted to carry a bitstream of the described example. Such a signal may be formatted as, for example, electromagnetic waves (e.g., using the radio frequency portion of a spectrum) or baseband signals. Formatting may include, for example, encoding a data stream and modulating a carrier wave with the encoded data stream. The information carried by the signal may be, for example, analog or digital information. It is well known that signals can be transmitted via a variety of different wired or wireless links. Signals may be stored on, or accessed or received from, a processor-readable medium.

[0118] This document describes numerous examples. Features of the examples may be provided individually or in any combination across various claim classes and types. Furthermore, examples may include one or more of the features, devices, or aspects described herein, individually or in any combination across various claim classes and types. For example, the features described herein may be implemented in a bitstream or signal that includes information generated as described herein. This information may allow a decoder to decode the bitstream, the encoder, bitstream, and / or decoder being any of the embodiments described. For example, the features described herein may be implemented by creating and / or transmitting and / or receiving and / or decoding a bitstream or signal. For example, the features described herein may be implemented by a method, process, apparatus, medium storing instructions, medium storing data, or signal. For example, the features described herein may be implemented by a TV, set-top box, mobile phone, tablet computer, or other electronic device performing decoding. The TV, set-top box, mobile phone, tablet computer, or other electronic device may display (e.g., using a monitor, screen, or other type of display) a resulting image (e.g., an image reconstructed from the residual of a video bitstream). The TV, set-top box, mobile phone, tablet computer, or other electronic device may receive a signal including an encoded image and perform decoding.

[0119] Systems, methods, and tools for performing chromaticity balancing using loop filters are disclosed. For example, a neural network-based filter can be used to filter (e.g., post-filter) a reconstructed image. The neural network-based filter can be used to complement or replace other filters. The neural network-based filter can be determined, for example, based on a neural network model. For example, one or more of the following can be used to train the neural network model: a training dataset, a default quantization parameter (QP), a QP offset, etc. Thinning frames and / or images can be determined based on the neural network-based filter.

[0120] A device (e.g., a video encoder, video decoder) can perform loop filter chroma balancing in association with a neural network-based filter. The device can obtain a reconstructed frame (e.g., an encoded reconstructed frame). Luminance and chroma components can be associated with the reconstructed frame. The device can decode the encoded reconstructed frame. The device can determine filter parameters associated with the reconstructed frame, for example, these filter parameters indicating the use of a neural network-based filter. The device can determine an input, for example, based on the reconstructed frame. The device can filter the input using a neural network model filter and balance information associated with the luminance and chroma components. The balance information can be obtained, for example, based on one or more of the following: attributes associated with the neural network-based filter; lookup tables; messages (e.g., information indicating the association with the neural network-based filter); etc. The device can determine a first QP offset (e.g., luminance QP offset) and a second QP offset (e.g., chroma QP offset) based, for example, on the balance information. The first QP offset and / or the second QP offset can be applied to the input before filtering with the neural network-based filter. The first QP offset can be the same as the second QP offset. Filtering the input can generate an output. The refinement frame can be determined and / or reconstructed based on the output. For example, a correction can be determined based on the output and a scaling factor. This correction can be added to the input. The refinement frame can be the correction added to the input. The device can include encoded refinement frames and / or indications of filter parameters associated with neural network-based filters in the video data.

[0121] This device can determine (e.g., create or train) a neural network model. It can train the neural network model to generate a trained neural network model. A neural network-based filter can be determined based on the neural network model or the trained neural network model. The device can train the neural network model based on a default balance ratio (e.g., associated with luminance / chrominance). The device can further refine the trained neural network model, for example, based on the trained neural network model and a QP offset.

[0122] The systems, methods, and tools described herein may relate to decoders. In some examples, the systems, methods, and tools described herein may relate to encoders. In some examples, the systems, methods, and tools described herein may relate to signals (e.g., from an encoder and / or received by a decoder). A computer-readable medium may include instructions for causing one or more processors to perform the methods described herein. A computer program product may include instructions that, when executed by one or more processors, cause one or more processors to perform the methods described herein.

[0123] In video coding, post-filtering can be applied to the reconstructed image to reduce coding artifacts and improve the rate-distortion tradeoff. This process can be a post-filter (e.g., in the outer loop of the codec) or an in-loop filter (e.g., in the codec loop). Neural network-based filters and / or learned filters can be used.

[0124] A generic input (e.g., for neural network-based filters) can be provided, determined, and / or used.

[0125] Video compression may include applying post-filters (e.g., in-loop post-filters) to images after they have been reconstructed or a portion thereof. Filters (e.g., several filters) may be applied to reconstructed samples of video images, for example, to reduce (e.g., to reduce) coding artifacts and reduce distortion of the original image. For example, deblocking filters (DBF) and / or sample adaptive offset (SAO) filters may be applied (e.g., sequentially) to the reconstructed samples. For example, filters (e.g., adaptive loop filters (ALF)) may be applied at the end of the process. Block-based filters (e.g., supplementary block-based filters), such as bilateral filters (BF), Hadamard filters, and / or diffusion filters, may be considered and / or used.

[0126] Figure 5 illustrates an example of a continuous loop filtering step. As shown in Figure 5, four continuous filters can be applied: DBF, bilateral filter, SAO, and ALF. The output can be a reconstructed image sample.

[0127] Figure 5 illustrates an example loop filtering process in an example video codec.

[0128] These different filters (e.g., typically) can be based on two processes (e.g., classification and filtering), which can be broken down into: pixel classification; determining filter parameters (e.g., DBF, SAO, ALF, but no BF), for example, determined by the encoder (encoder only); encoding / decoding filter parameters (e.g., DBF, SAO, ALF, but no BF); class correlation filtering; and so on.

[0129] A neural network (NN)-based post-filter can be used. NN-based post-filters can replace one or more loop filters, or can be added to an existing loop filter.

[0130] It can enable, provide, execute and / or use neural network video codecs (NNVC).

[0131] Neural network-based filtering can be used for NNVC.

[0132] Convolutional neural networks (NNs) can be added to or replaced in loop filters, for example, after a DBF filter.

[0133] Figure 6 illustrates an example CNN loop filter process in NNVC.

[0134] Figure 6 illustrates an example of a pipeline for loop filtering. As shown in Figure 6, the reconstructed frame can be processed by DBF (e.g., deblocking). The output can be used as input 1 as well as other inputs (e.g., predicted frame, residual, partition map, QP map, etc.). In the example, the input can be obtained before DBF.

[0135] Frames can be filtered (e.g., block-wise). A correction can be generated. This correction can be modulated by a scaling factor. This correction can be added to the input. The correction can be modulated by a scaling factor and added to the input. An ALF filter can (e.g., optionally) be applied to the result, and the final frame can be the output.

[0136] In this process, the CNN may have been trained (e.g., learned) offline, for example, on a large block dataset.

[0137] The training process can be provided, executed, and / or used.

[0138] Figure 7 illustrates an example training process for learning a CNN.

[0139] An example learning process can be provided, as shown in Figure 7.

[0140] Large block datasets can be used to train models using inputs extracted from encodings (e.g., real number encodings). The loss can be computed (e.g., typically calculated) as mean squared error (MSE) or mean absolute error (MAE).

[0141] The training process can typically be divided into different stages (e.g., as shown in Figure 8). The network can be trained (e.g., first) on a dataset of reconstructed images, for example, using a default video codec. This dataset can include inputs (e.g., all necessary inputs) and the original images. The resulting model can (e.g., then) be integrated into the codec. For example, considering the trained model, a generated (e.g., new) dataset can be produced.

[0142] For example, considering the model's impact on predictions, this generated (e.g., new) dataset can be used to retrain the model.

[0143] This entire process can be iterated and / or divided into several sub-stages (e.g., first training only on intra-frames, then training on all frames). The entire process can be lengthy, resource-intensive, and / or burdensome, for example, because the dataset may be generated by re-encoding the dataset (e.g., the complete dataset).

[0144] Figure 8 shows an example of two-stage training.

[0145] For NNVC loop filters or post-filters, the filter can be trained, for example, by minimizing (e.g., directly minimizing) the error (e.g., loss) between the original samples and the filtered samples, typically using the MSE norm. In the case of a joint luminance / chrominance network, the loss can take into account both the luminance and chrominance components in the filtered samples.

[0146] The loss can be determined according to Equation 1.

[0147]

[0148] Equation 1, for example, Y, U, and V can be the original samples of the luminance and chrominance components. For example, Y', U', and V' can be filtered samples. For example, α Y α U and α V This can be the weights applied to (e.g., each) component during the training phase. In the example, the following relationship can be used: α Y =6 and α U =α V =1. For example, |.| can be a norm operator (e.g., using L2 norm or L1 norm).

[0149] The balance can be fixed and can be determined during training. The gains between each component may be unbalanced (e.g., not necessarily balanced). If training is performed on a large dataset, the entire training process may need to be redone if, for example, the luminance / chroma balance changes (e.g., needs to be changed).

[0150] The luminance / chrominance gain balance of a network can be associated with the network, for example, so that the device (e.g., encoder) takes that balance into account.

[0151] In the example, the associated QP chromaticity offset can be associated with the model, for example, to balance luminance and chromaticity gain.

[0152] For example, if (e.g., when) the model is used as a post-filter, the aforementioned information (e.g., as described herein) can be used in the Supplemental Enhancement Information (SEI) message.

[0153] For example, during training (e.g., to shorten the training phase), the final phase can be performed (e.g., only the final phase is performed) using an adaptive QP chroma offset.

[0154] In the example, for instance, QP chroma offset adaptation can be performed (e.g., completed) instead of loss-weighted adaptation.

[0155] It can provide, determine, and / or use luminance / chromaticity balance information.

[0156] In the example, a balance between luminance and chrominance can be considered (e.g., on the encoder side). For example, a filter may have (e.g., determined / known to have) a bdrate gain of 10% on luminance and a bdrate gain of 25% on chrominance (e.g., on a given dataset).

[0157] In video codecs, the correspondence between QP chroma offset and the gain balance between luminance and chroma can be stable. Increasing the QP chroma offset by 1 (e.g., the same increase on each component Cb and Cr) may decrease the chroma bdrate gain (e.g., about 10%) and may increase the luminance bdrate gain (e.g., about 1%).

[0158] In the example, this information can be used (e.g., by the encoder) to balance the luma / chroma gain. For example, the QP chroma offset can be adapted to balance the gain (e.g., if / when a filter is applied). As shown in the accompanying figures, the QP chroma offset can be increased by 1 (e.g., during the encoding stage), for example, so that the final filtered frame can have a balanced luma / chroma gain.

[0159] In the example, this adaptation can be inferred (e.g., directly inferred) based on filter activation. For example, if (e.g., when) the filter is on, an offset (e.g., a fixed, known offset) can be added to the QP offset (e.g., without signaling, no signaling is required). This offset can depend on the model's properties and / or the luminance / chroma balance gain. In the example, the adaptation can depend on a lookup table (LUT), which may depend on the QP of a slice or block.

[0160] Table 1 shows an example of QP chroma offset adaptation that depends on neural network filter indications (e.g., the nnfilter enable flag) in the slice header syntax. As an example, this syntax can be added on top of other video syntaxes.

[0161]

[0162] The variables cb_qp_offset_filter and cr_qp_offset_filter in Table 1 can be determined (e.g., inferred) based on the following. For example, if sh_nnfilter_enabled_flag is false, both variables can be 0 (e.g., QP offset adaptation). If sh_nnfilter_enabled_flag is true, then it can be determined that cb_qp_offset_filter = 1 and cr_qp_offset_filter = 1.

[0163] Here, the value 1 can be used as an example, but it can be any value, depending on the inherent properties of the NN filter (e.g., these properties can be determined / known and fixed after training is complete).

[0164] The colorimetric parameters Qp′ of the Cb and Cr components can be determined (e.g., derived) according to equations 2 to 4. Cb and Qp′ Cr and the colorimetric parameter Qp′ of the joint Cb-Cr encoding CbCr .

[0165]

[0166] Equation 2

[0167] Equation 3

[0168] In Equation 4, the luminance / chroma balance can be calculated as the average quality gain (e.g., per QP) for each component, in dB. In the example, a ratio of the quality gain can be used (e.g., a ratio only). For example, the average gain (in dB) over the dataset using a post-filter can be calculated for each component (e.g., for one / each QP). The gain ratio can be used to make encoding decisions and QP chroma shifts.

[0169] SEI information can be identified, provided, and / or used.

[0170] Information about luminance / chromaticity balance can be carried in, for example, the SEI message describing the post-filter (e.g., in the case of post-filtering).

[0171] Table 2 shows an example where the value of the nnrpf_luma_chroma_balancing variable can be used to perform quality balancing of luma / chroma information on a post-NN filter that outputs at least the luma and chroma components (e.g., nnrpf_out_order_idc is greater than 1). This variable can be the quality gain ratio between luma and chroma; for example, 1 indicates the same quality improvement, 2 indicates a more than 2x improvement in chroma, and so on.

[0172]

[0173] Table 2 shows the training process that can be performed, provided, and / or used.

[0174] Training can be used, for example, to accelerate multi-stage training processes (e.g., in the case of adaptive content filtering, where fine-tuning training should be performed based on the content).

[0175] For example, a filter can be assumed where the luminance / chrominance balance makes the chrominance gain twice that of the luminance gain. Retraining the model using different weights between luminance and chrominance in the loss function (e.g., full retraining) can be used to rebalance the gains. This process can be flexible, for example, as shown in Figure 9.

[0176] The first stage or the first N stages can be performed using the default luminance / chrominance balance.

[0177] During the final stage, QP chromaticity offsets can be used from a dataset adapted for the final stage (e.g., the final dataset). For example, performing luminance / chromaticity balancing can be avoided (e.g., it can be performed in stage 2), or luminance / chromaticity balancing with QP+1 and +2 (e.g., as in stage 2b). This dataset can be used to train (e.g., fine-tune) a model with the desired luminance / chromaticity gain balance, where the loss can be adapted to the desired balance.

[0178] In the example, dataset adaptation can be performed, but the loss weighting can remain unchanged.

[0179] Figure 9 shows an example dataset adaptation for balancing luminance / chrominance gain.

[0180] For example, in the case of an adaptive post-filter, a training process (e.g., as described herein) can be performed on (e.g., each) different (e.g., new) encoded sequences (e.g., making it part of the encoding process).

[0181] The model can be adapted. In the example, the model can be modified (e.g., adapted) to take into account the QP of (e.g., each) component.

[0182] Figure 10 illustrates an example of a high-computation-point model used in a hybrid neural network video codec. NN-based filtering can receive (e.g., acquire) one or more of the following as input (e.g., as shown in Figure 10): the type of encoding mode (e.g., IBP); the QP of a slice (e.g., QPslide); the QP of a sequence (e.g., QPbase); boundary strength (BS); prediction of a block (Pred); reconstruction of a block (Rec); and so on.

[0183] The QP of each component can be added (e.g., added independently). The dataset can be adapted, for example, by generating multiple versions of frames with different QP chromaticity offsets. Patches with multiple luminance / chromaticity balances can be provided (e.g., in the dataset). Patches with multiple luminance / chromaticity balances can be used during training, for example, to account for the original luminance / chromaticity balance of the codec.

[0184] In the example, the weighting of the loss can depend on the QP difference between luminance and chrominance, for example, to account for the desired luminance / chrominance balance. For example, if (e.g., when) QP luminance and QP chrominance are equal, the loss weighting for luminance Cb and Cr can be 6:1:1, respectively. For example, if (e.g., when) QP chrominance is 1 greater than QP luminance, the loss weighting can be 10:1:1.

[0185] In the example, for instance, if (e.g., when) adaptive QP is used, QP slices can be replaced with patched QP or average QP.

[0186] Although the features and elements have been described above in specific combinations, those skilled in the art will understand that each feature or element can be used alone or in any combination with other features and elements. Furthermore, the methods described herein can be implemented in a computer program, software, or firmware 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 discs and digital multifunction disks (DVDs). The processor associated with the software can be used to implement a radio frequency transceiver for a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

1. A video decoding device, the video decoding device comprising: A processor configured to: obtain an encoded reconstructed frame, wherein a luminance component and a chrominance component are associated with the reconstructed frame; decode the encoded reconstructed frame, wherein decoding the encoded reconstructed frame generates a reconstructed frame and filter parameters associated with the reconstructed frame, wherein the filter parameters indicate the use of a neural network model filter; determine an input based on the reconstructed frame; filter the input using the neural network model filter and balance information associated with the luminance component and the chrominance component, wherein filtering the input using the neural network model filter and the balance information generates an output; and reconstruct a thinned frame based on the output.

2. The video decoding device according to claim 1, wherein, The processor is further configured to: obtain the balance information associated with the luminance component and the chrominance component; and determine a first quantization parameter (QP) offset and a second QP offset based on the balance information, wherein the first QP offset and the second QP offset are applied to the input before filtering is performed using the neural network model filter.

3. The video decoding device according to claim 2, wherein, The balance information associated with the luminance component and the chrominance component is determined based on one of the attributes, lookup tables, and messages associated with the neural network model filter, wherein the messages indicate information associated with the neural network model filter.

4. The video decoding device according to any one of claims 2 to 3, wherein, The first QP offset is associated with the luminance component, and the second QP offset is associated with the chrominance component.

5. The video decoding device according to claim 4, wherein, The first QP offset is the same as the second QP offset.

6. The video decoding device according to any one of claims 1 to 5, wherein, The processor is further configured to: determine a neural network model; and determine a trained neural network model based on training the neural network model, wherein a neural network model filter is determined based on the trained neural network model.

7. The video decoding device according to claim 6, wherein, Training the neural network model includes determining a first training dataset based on a default balancing ratio and a second training dataset, and determining a third dataset based on the first training dataset and a QP offset.

8. The video decoding device according to any one of claims 1 to 7, wherein, The processor is also configured to determine a correction based on the output and a scaling factor, wherein the refined frame is reconstructed based on adding the correction to the input.

9. A video decoding method, the video decoding method comprising: Obtain an encoded reconstructed frame, wherein a luminance component and a chrominance component are associated with the reconstructed frame; decode the encoded reconstructed frame, wherein decoding the encoded reconstructed frame generates a reconstructed frame and filter parameters associated with the reconstructed frame, wherein the filter parameters indicate the use of a neural network model filter; determine an input based on the reconstructed frame; filter the input using the neural network model filter and balance information associated with the luminance component and the chrominance component, wherein filtering the input using the neural network model filter and the balance information generates an output; and reconstruct a thinned frame based on the output.

10. The video decoding method according to claim 9, wherein, The video decoding method further includes: obtaining the balance information associated with the luminance component and the chrominance component; and determining a first quantization parameter (QP) offset and a second QP offset based on the balance information, wherein the first QP offset and the second QP offset are applied to the input before filtering using the neural network model filter.

11. The video decoding method according to claim 10, wherein, The balance information associated with the luminance component and the chrominance component is determined based on one of the attributes, lookup tables, and messages associated with the neural network model filter, wherein the messages indicate information associated with the neural network model filter.

12. The video decoding method according to any one of claims 10 to 11, wherein, The first QP offset is associated with the luminance component, and the second QP offset is associated with the chrominance component.

13. The video decoding method according to claim 12, wherein, The first QP offset is the same as the second QP offset.

14. The video decoding method according to any one of claims 9 to 13, wherein, The video decoding method further includes: determining a neural network model; and determining a trained neural network model based on training the neural network model, wherein the neural network model filter is determined based on the trained neural network model.

15. The video decoding method according to claim 14, wherein, Training the neural network model includes determining a first training dataset based on a default balancing ratio and a second training dataset, and determining a third dataset based on the first training dataset and a QP offset.

16. The video decoding method according to any one of claims 9 to 15, wherein, The video decoding method further includes: determining correction based on the output and the scaling factor, wherein the refined frame is reconstructed based on adding the correction to the input.

17. A video encoding device, the video encoding device comprising: A processor configured to: obtain a reconstructed frame, wherein a luminance component and a chrominance component are associated with the reconstructed frame; determine an input based on the reconstructed frame; filter the input using a neural network model filter and balance information associated with the luminance component and the chrominance component, wherein the filtering of the input using the neural network model filter and the balance information generates an output; determine a thinning frame based on the output; and include in video data an encoded thinning frame and an indication of filter parameters associated with the neural network model filter.

18. The video encoding apparatus according to claim 17, wherein, The processor is further configured to: obtain the balance information associated with the luminance component and the chrominance component; and determine a first quantization parameter (QP) offset and a second QP offset based on the balance information, wherein the first QP offset and the second QP offset are applied to the input before filtering is performed using the neural network model filter.

19. The video encoding device according to claim 18, wherein, The balance information associated with the luminance component and the chrominance component is determined based on one of the attributes, lookup tables, and messages associated with the neural network model filter, wherein the messages indicate information associated with the neural network model filter.

20. The video encoding apparatus according to any one of claims 18 to 19, wherein, The first QP offset is associated with the luminance component, and the second QP offset is associated with the chrominance component.

21. The video encoding device according to claim 20, wherein, The first QP offset is the same as the second QP offset.

22. The video encoding apparatus according to any one of claims 17 to 21, wherein, The processor is further configured to: determine a neural network model; and determine a trained neural network model based on training the neural network model, wherein a neural network model filter is determined based on the trained neural network model.

23. The video encoding device according to claim 22, wherein, Training the neural network model includes determining a first training dataset based on a default balancing ratio and a second training dataset, and determining a third dataset based on the first training dataset and a QP offset.

24. The video encoding apparatus according to any one of claims 17 to 23, wherein, The processor is also configured to determine a correction based on the output and a scaling factor, wherein the refined frame is reconstructed based on adding the correction to the input.

25. A video encoding method, the video encoding method comprising: Obtain a reconstructed frame, wherein a luminance component and a chrominance component are associated with the reconstructed frame; determine an input based on the reconstructed frame; filter the input using a neural network model filter and balance information associated with the luminance component and the chrominance component, wherein filtering the input using the neural network model filter and the balance information generates an output; determine a thinning frame based on the output; and include the encoded thinning frame and an indication of filter parameters associated with the neural network model filter in the video data.

26. The video encoding method according to claim 25, wherein, The video encoding method further includes: obtaining the balance information associated with the luminance component and the chrominance component; and determining a first quantization parameter (QP) offset and a second QP offset based on the balance information, wherein the first QP offset and the second QP offset are applied to the input before filtering using the neural network model filter.

27. The video encoding method according to claim 26, wherein, The balance information associated with the luminance component and the chrominance component is determined based on one of the attributes, lookup tables, and messages associated with the neural network model filter, wherein the messages indicate information associated with the neural network model filter.

28. The video coding method according to any one of claims 26 to 27, wherein, The first QP offset is associated with the luminance component, and the second QP offset is associated with the chrominance component.

29. The video encoding method according to claim 28, wherein, The first QP offset is the same as the second QP offset.

30. The video coding method according to any one of claims 25 to 29, wherein, The video encoding method further includes: determining a neural network model; and determining a trained neural network model based on training the neural network model, wherein a neural network model filter is determined based on the trained neural network model.

31. The video encoding method according to claim 30, wherein, Training the neural network model includes determining a first training dataset based on a default balancing ratio and a second training dataset, and determining a third dataset based on the first training dataset and a QP offset.

32. The video coding method according to any one of claims 25 to 31, wherein, The video encoding method further includes: determining correction based on the output and a scaling factor, wherein the refined frame is reconstructed based on adding the correction to the input.

Images