Multiple prediction models and signaling in video coding

Abstract

A method of encoding or decoding video data includes, for each model of a plurality of models, deriving a respective model parameter associated with a respective corresponding model of the plurality of models using a first set of samples; determining a respective cost value associated with the corresponding model of the plurality of models using the respective model parameters and a second set of samples to generate respective cost values for the plurality of models, where the first set of samples and the second set of samples are mutually exclusive from each other; determining, based on the respective cost values, a model of the plurality of models for encoding or decoding the current block; and encoding or decoding the current block based on the determined model.

Description

[0001] This application claims priority to U.S. Patent Application No. 18 / 772,917, filed July 15, 2024, and U.S. Provisional Application No. 63 / 514,927, filed July 21, 2023, the entire contents of which are incorporated herein by reference. U.S. Patent Application No. 18 / 772,917, filed July 15, 2024, claims the benefit of U.S. Provisional Application No. 63 / 514,927, filed July 21, 2023. Technical Field

[0002] This disclosure relates to video encoding and video decoding. Background Technology

[0003] Digital video capabilities can be incorporated into a wide variety of devices, including digital televisions, digital live broadcast systems, wireless broadcasting systems, personal digital assistants (PDAs), laptops or desktop computers, tablets, e-book readers, digital cameras, digital recording devices, digital media players, video game devices, video game consoles, cellular or satellite wireless phones (so-called "smartphones"), video conferencing equipment, video streaming devices, and more. Digital video devices implement video decoding technologies, such as those defined by MPEG-2, MPEG-4, ITU-T H.263, ITU-T H.264 / MPEG-4 (Part 10, Advanced Video Decoding (AVC)), ITU-T H.265 / High Efficiency Video Decoding (HEVC), ITU-T H.266 / Variety Video Decoding (VVC) and extensions to these standards, as well as proprietary video codecs / formats such as AOMedia Video1 (AV1) developed by the Open Media Alliance. By implementing such video decoding technologies, video devices can more efficiently send, receive, encode, decode, and / or store digital video information.

[0004] Video decoding techniques include spatial (intra-picture) prediction and / or temporal (inter-picture) prediction to reduce or remove redundancy inherent in video sequences. For block-based video decoding, video slices (e.g., video pictures or portions of video pictures) can be divided into video blocks, which may also be referred to as decoding tree units (CTUs), decoding units (CUs), and / or decoding nodes. Video blocks in a slice after intra-frame decoding (I) of a picture are encoded using spatial prediction relative to reference samples in adjacent blocks within the same picture. Video blocks in a slice after inter-frame decoding (P or B) of a picture can use spatial prediction relative to reference samples in adjacent blocks within the same picture or temporal prediction relative to reference samples in other reference pictures. A picture may be referred to as a frame, and a reference picture may be referred to as a reference frame. Summary of the Invention

[0005] In general, this disclosure describes techniques for determining a model for generating predictions (e.g., prediction blocks) for video decoding. In video decoding, the current block of video data is predicted based on prediction blocks, and various ways of generating such prediction blocks are possible, such as model-based methods. This disclosure describes example techniques for determining (e.g., with minimal to no signaling) the model used to generate the prediction. For example, a video encoder and a video decoder may determine a cost value associated with each model in the model and determine the model to be used based on that cost value. Utilizing the example techniques described in this disclosure, the amount of information transmitted via signaling can be reduced, and thus bandwidth utilization can be reduced.

[0006] For the current block to be encoded or being decoded, the video encoder and the video decoder may use a first set of samples to derive model parameters for each model in the model. In one or more examples, the video encoder and the video decoder may use a second set of samples and the corresponding model parameters to determine the cost value associated with each model in the model. For example, the video encoder and the video decoder may apply the model parameters to the second set of samples to generate a predicted sample and determine the cost value based on the difference between the predicted sample and the second set of samples. In one or more examples, the first set of samples and the second set of samples may be different. For example, the first set of samples and the second set of samples may be mutually exclusive.

[0007] In one example, this disclosure describes a method for encoding or decoding video data, the method comprising: for each of a plurality of models, using a first set of samples to derive a corresponding model parameter associated with a corresponding model in the plurality of models; using the corresponding model parameter and a second set of samples to determine a corresponding cost value associated with the corresponding model in the plurality of models to generate a corresponding cost value for the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; determining, based on the corresponding cost value, a model in the plurality of models for encoding or decoding the current block; and encoding or decoding the current block based on the determined model.

[0008] In one example, this disclosure describes an apparatus for encoding or decoding video data, the apparatus comprising: one or more memories configured to store the video data; and processing circuitry coupled to the one or more memories, wherein the processing circuitry is configured to: for each of a plurality of models, derive corresponding model parameters associated with a corresponding model among the plurality of models using a first set of samples; determine a corresponding cost value associated with the corresponding model among the plurality of models using the corresponding model parameters and a second set of samples to generate corresponding cost values ​​for the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; determine a model among the plurality of models for encoding or decoding the current block based on the corresponding cost value; and encode or decode the current block based on the determined model.

[0009] In one example, this disclosure describes one or more computer-readable storage media on which instructions are stored, which, when executed, cause one or more processors to: for each of a plurality of models, derive a corresponding model parameter associated with a corresponding model in the plurality of models using a first set of samples; determine a corresponding cost value associated with the corresponding model in the plurality of models using the corresponding model parameter and a second set of samples to generate a corresponding cost value for the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; determine, based on the corresponding cost value, a model in the plurality of models for encoding or decoding the current block; and encode or decode the current block based on the determined model.

[0010] Details of one or more examples are set forth in the accompanying drawings and the following description. Other features, objects, and advantages will be apparent from the description, drawings, and claims. Attached Figure Description

[0011] Figure 1 This is a block diagram illustrating an example video encoding and decoding system that can perform the techniques of this disclosure.

[0012] Figure 2 This is a block diagram illustrating an example video encoder that can perform the techniques of this disclosure.

[0013] Figure 3 This is a block diagram illustrating an example video decoder that can perform the techniques of this disclosure.

[0014] Figure 4 This is a flowchart illustrating an example method for encoding the current block according to the technology of this disclosure.

[0015] Figure 5 This is a flowchart illustrating an example method for decoding the current block according to the technology of this disclosure.

[0016] Figure 6 This is a conceptual diagram illustrating an example of the spatial portion of a convolutional filter.

[0017] Figure 7 This is a conceptual diagram illustrating an example of a reference region filled with information for deriving filter coefficients.

[0018] Figure 8 This is a conceptual diagram illustrating a gradient- and position-based convolutional cross-component model (GL-CCCM).

[0019] Figure 9 This is a conceptual diagram illustrating the luminance samples associated with the chromaticity samples.

[0020] Figure 10 This is a conceptual map illustrating the locations within a reconstructed sample.

[0021] Figure 11A and Figure 11B This is a conceptual diagram illustrating the reference region of a block vector guided CCCM (BVG-CCCM).

[0022] Figure 12 This is a concept diagram illustrating samples used for model validation.

[0023] Figure 13 This is a flowchart illustrating one or more examples as described in this disclosure. Detailed Implementation

[0024] In video decoding, the video encoder and video decoder determine predictions (e.g., prediction blocks) for inter-frame or intra-frame prediction of the current block. For example, the video encoder and video decoder perform the same technique to generate prediction blocks based on previously encoded or decoded samples. The video encoder determines residual values ​​indicating the differences between the current block and the prediction block and sends information indicating the residual values ​​via a signal. The video decoder receives the information indicating the residual values ​​and adds the residual values ​​to the prediction block to reconstruct the current block.

[0025] There may be various ways in which video encoders and video decoders can generate prediction blocks. For example, the current block may include a luma component (e.g., a luma current block) and a chroma component (e.g., a chroma current block). It may be possible to use the luma current block to generate a prediction block for the chroma current block. Furthermore, it may be possible for the video encoder and video decoder to first determine a reference block and then modify (e.g., filter, adjust, etc.) the reference block to generate the prediction block.

[0026] Various video decoding techniques define how prediction blocks are generated, such as the High Efficiency Video Decoding (HEVC) standard, the Multi-Functional Video Decoding (VVC) standard, the Enhanced Compression Model (ECM), and video decoding techniques in AV1 and AV2. This disclosure relates to prediction in video codecs, and in some examples, it is applicable to HEVC, VVC, ECM, AV1, AV2, and similar video codecs.

[0027] The various ways to generate prediction blocks can be referred to as models. In ECM, there are several tools that utilize model derivation at the video encoder and video decoder to form predictions. Examples of such tools are Local Intensity Compensation (LIC), Linear Model (LM), and Convolutional Cross Component Model (CCCM), where the model is derived by using reconstructed neighboring luma and chroma samples to derive the cross component model or by using neighboring samples and neighboring samples of the reference block to derive a model for inter-frame prediction (e.g., prediction based on samples in images other than the image containing the current block).

[0028] Generally, as used in this disclosure, "model" refers to an encoding or decoding technique in which a video encoder and a video decoder apply model parameters (e.g., scaling and offset) to sample values ​​to generate a predicted block for the current block being encoded or decoded. The sample values ​​may be based on previously reconstructed samples, and the model parameters may be applied to a portion of the pixel domain (e.g., from the perspective of the video encoder before transform or quantization and from the perspective of the video decoder after inverse quantization and inverse transform). The video encoder and video decoder may determine the model parameters based on previously reconstructed samples. In some examples, the video encoder may transmit a signal, and the video decoder may receive the model parameters.

[0029] In model-based video decoding, three operations can be employed to determine the prediction block (e.g., the prediction signal) for the current block. The first operation can be determining model parameters. The second operation can be determining samples to which the model parameters are applied. The third operation can be applying the model parameters (e.g., performing scaling and / or offsetting using the model parameters) to the determined samples to generate the prediction block. The samples to which the model parameters are applied can be previously reconstructed samples. For example, in cross-component model-based video decoding, the current block can be a chroma block, and the previously reconstructed samples can be downsampled (if needed) luminance samples based on the luminance block.

[0030] As noted, multiple models may exist. This disclosure describes example techniques for reducing the amount of signaling required to indicate the model to be used. For example, one way to indicate the model is by signaling syntax elements in the bitstream. That is, the video encoder signals syntax elements indicating the model to be used, and the video decoder receives the syntax elements and determines the model to be used based on the syntax elements. However, since the number of models can be relatively large, the signaling overhead of the syntax elements can increase.

[0031] In one or more examples, a video encoder and a video decoder may determine a corresponding cost value associated with a corresponding model among multiple models, determine a model for decoding the current block based on the corresponding cost value, and decode the current block based on the determined model. Various example ways may exist, such as determining the cost value based on target samples (e.g., neighboring samples) of the current block.

[0032] For example, as described above, in model-based video decoding, the video encoder and video decoder can each determine model parameters. In one or more examples, the video encoder and video decoder can use a first set of samples to derive the corresponding model parameters associated with corresponding models in a plurality of models. For example, the video encoder and video decoder can determine first model parameters of a first model based on the first set of samples, determine second model parameters of a second model based on the first set of samples, and so on. For each model in the models, the first set of samples used to determine the model parameters does not necessarily have to be the same, because different models can use different samples to derive the model parameters. However, for each model in the models, the first set of samples may be the same.

[0033] The video encoder and video decoder can use the corresponding model parameters and the second set of samples to determine the corresponding cost values ​​associated with the corresponding models among a plurality of models, thereby generating the corresponding cost values ​​for the plurality of models. The first set of samples and the second set of samples may be mutually exclusive in some examples, partially overlap in some examples, or be identical in some examples.

[0034] As an example, a video encoder and a video decoder may generate predicted samples for a second set of samples based on model parameters (e.g., model parameters derived using a first set of samples). For example, the video encoder and video decoder may determine a first predicted sample for a first model based on first model parameters used for a first model, but for the second set of samples, determine a first predicted sample; and determine a second predicted sample for a second model based on second model parameters used for a second model, but for the second set of samples, determine a second predicted sample, and so on.

[0035] Since the first and second sets of samples have already been reconstructed, the generated prediction samples for the second set of samples are used for model selection purposes. For example, even though the second set of samples has been fully reconstructed, the video encoder and decoder can perform operations such as encoding or decoding the second set of samples, like generating prediction samples for the second set. However, the second set of samples is known, and therefore, the encoding and decoding process is for model selection. Furthermore, these generated prediction samples for the second set of samples may not be the same prediction samples used to encode or decode the current block.

[0036] The video encoder and video decoder can determine the difference between the generated predicted samples and the second set of samples to determine the corresponding cost value. For example, the video encoder and video decoder can determine a first cost value based on the sum of absolute differences (SAD) between the second set of samples and the first predicted samples used for the first model, determine a second cost value based on the SAD between the second set of samples and the second predicted samples used for the second model, and so on.

[0037] The video encoder and decoder can determine, based on a corresponding cost value, which model from among several models to use for encoding or decoding the current block. The video encoder and decoder can then encode or decode the current block based on the determined model. That is, the example techniques described above can assist in model selection, but once a model is selected, the video encoder and decoder can perform operations consistent with the determined model to generate a predicted block for encoding or decoding the current block.

[0038] For example, the video encoder and video decoder can encode or decode the current block using either an inter-frame prediction mode or an intra-frame prediction mode. For instance, after determining a model for decoding the current block using the example techniques described above, the video encoder and video decoder can perform operations to generate a predicted block for the current block using the determined model. If the determined model requires the video encoder and video decoder to access samples from another image besides the image containing the current block (e.g., inter-frame prediction) to generate a predicted block for the current block, the video encoder and video decoder can perform such operations consistent with the determined model. If the determined model requires the video encoder and video decoder to access samples from the same image as the image containing the current block (e.g., intra-frame prediction or intra-block copy prediction) to generate a predicted block for the current block, the video encoder and video decoder can perform such operations consistent with the determined model.

[0039] By utilizing example techniques, it can be possible to reduce signaling overhead. For instance, in some examples, the video encoder and decoder can utilize the model with the lowest cost, in which case the video decoder may not need to signal the syntax elements defining the model. As another example, the video encoder and decoder can each construct a list of candidate models based on a corresponding cost value (e.g., sorting the model list in ascending order based on the cost value). That is, the video encoder and decoder can construct a list of candidate models where candidates are sorted based on corresponding cost values. The video encoder can signal the index of the model list, which may require less signaling overhead compared to signaling the syntax elements. As yet another example, the video encoder and decoder can associate at least one model in the model list with an index of the current block or a list of candidate models that identifies the model and store information indicating this association. The video encoder and decoder can decode subsequent blocks based on the information indicating this association, which can reduce the amount of signaling used for decoding subsequent blocks.

[0040] Figure 1 This is a block diagram illustrating an example video encoding and decoding system 100 capable of performing the techniques of this disclosure. The techniques of this disclosure generally involve decoding (encoding and / or decoding) video data. Typically, video data includes any data used for processing video. Therefore, video data may include unencoded raw video, encoded video, decoded (e.g., reconstructed) video, and video metadata, such as signaling data.

[0041] like Figure 1 As shown, in this example, system 100 includes a source device 102 that provides encoded video data to be decoded and displayed by a destination device 116. Specifically, source device 102 provides the video data to destination device 116 via computer-readable medium 110. Source device 102 and destination device 116 can be or may include any of a wide range of devices, such as desktop computers, laptop computers, mobile devices, tablet computers, set-top boxes, mobile phones such as smartphones, televisions, cameras, display devices, digital media players, video game consoles, video streaming devices, broadcast receiver devices, etc. In some cases, source device 102 and destination device 116 may be configured for wireless communication and are therefore referred to as wireless communication devices.

[0042] exist Figure 1In the example, source device 102 includes a video source 104, memory 106, video encoder 200, and output interface 108. Destination device 116 includes an input interface 122, video decoder 300, memory 120, and display device 118. According to this disclosure, the video encoder 200 of source device 102 and the video decoder 300 of destination device 116 can be configured to apply techniques for using multiple prediction models and signaling in video decoding. Thus, source device 102 represents an example of a video encoding device, while destination device 116 represents an example of a video decoding device. In other examples, the source device and destination device may include other components or arrangements. For example, source device 102 may receive video data from an external video source such as an external camera. Similarly, destination device 116 may interface with an external display device, rather than including an integrated display device.

[0043] like Figure 1 The system 100 shown is merely an example. Typically, any digital video encoding and / or decoding device can perform techniques for using multiple predictive models and signaling in video decoding. Source device 102 and destination device 116 are merely examples of such decoding devices, where source device 102 generates decoded video data for transmission to destination device 116. This disclosure refers to a “decoding” device as a device that performs the decoding (e.g., encoding and / or decoding) of data. Thus, video encoder 200 and video decoder 300 represent examples of decoding devices, specifically, a video encoder and a video decoder, respectively. In some examples, source device 102 and destination device 116 may operate in a substantially symmetrical manner, such that each of source device 102 and destination device 116 includes video encoding and decoding components. Therefore, system 100 may support one-way or two-way video transmission between source device 102 and destination device 116, for example, for video streaming, video playback, video broadcasting, or video telephony.

[0044] Typically, video source 104 represents the source of video data (i.e., unencoded raw video data) and provides a sequential series of pictures (also referred to as "frames") of the video data to video encoder 200, which encodes the data for the pictures. Video source 104 of source device 102 may include video capture devices such as cameras, video archives containing previously captured raw video, and / or video feed interfaces for receiving video from video content providers. Alternatively, video source 104 may generate computer graphics-based data as source video, or a combination of live video, archived video, and computer-generated video. In each case, video encoder 200 encodes the captured, pre-captured, or computer-generated video data. Video encoder 200 may rearrange the pictures from the received order (sometimes referred to as "display order") to a decoding order for decoding. Video encoder 200 may generate a bitstream comprising the encoded video data. Then, the source device 102 can output the encoded video data to the computer-readable medium 110 via the output interface 108 for reception and / or retrieval by, for example, the input interface 122 of the destination device 116.

[0045] The memory 106 of source device 102 and the memory 120 of destination device 116 represent general-purpose memory. In some examples, memories 106 and 120 may store raw video data, such as raw video from video source 104 and raw decoded video data from video decoder 300. Additionally or alternatively, memories 106 and 120 may store software instructions executable by, for example, video encoder 200 and video decoder 300. Although memories 106 and 120 are shown separately from video encoder 200 and video decoder 300 in this example, it should be understood that video encoder 200 and video decoder 300 may also include internal memory for functionally similar or equivalent purposes. Furthermore, memories 106 and 120 may store encoded video data, such as output from video encoder 200 and input to video decoder 300. In some examples, portions of memories 106 and 120 may be allocated as one or more video buffers, for example, to store raw decoded and / or encoded video data.

[0046] Computer-readable medium 110 may represent any type of medium or device capable of transmitting encoded video data from source device 102 to destination device 116. In one example, computer-readable medium 110 represents a communication medium that enables source device 102 to directly transmit encoded video data to destination device 116 in real time, for example, via a radio frequency network or a computer-based network. According to a communication standard such as a wireless communication protocol, output interface 108 may modulate the transmitted signal including the encoded video data, and input interface 122 may demodulate the received transmitted signal. The communication medium may include any wireless or wired communication medium, such as radio frequency (RF) spectrum or one or more physical transmission lines. The communication medium may form part of a packet-based network such as a local area network, a wide area network, or a global network (such as the Internet). The communication medium may include a router, switch, base station, or any other equipment that may be useful for facilitating communication from source device 102 to destination device 116.

[0047] In some examples, source device 102 can output encoded data from output interface 108 to storage device 112. Similarly, destination device 116 can access encoded data from storage device 112 via input interface 122. Storage device 112 may include any data storage medium of various distributed or locally accessed data storage media, such as hard disk drives, Blu-ray discs, DVDs, CD-ROMs, flash memory, volatile or non-volatile memory, or any other suitable digital storage medium for storing encoded video data.

[0048] In some examples, source device 102 can output encoded video data to file server 114 or another intermediate storage device that can store the encoded video data generated by source device 102. Destination device 116 can access the stored video data from file server 114 via streaming or download.

[0049] File server 114 can be any type of server device capable of storing encoded video data and sending the encoded video data to destination device 116. File server 114 may represent a web server (e.g., for a website), a server configured to provide file transfer protocol services (such as File Transfer Protocol (FTP) or FLUTE-based file delivery protocol), a Content Delivery Network (CDN) device, a Hypertext Transfer Protocol (HTTP) server, a Multimedia Broadcast Multicast Service (MBMS) or Enhanced MBMS (eMBMS) server, and / or a Network Attached Storage (NAS) device. File server 114 may additionally or alternatively implement one or more HTTP streaming protocols, such as HTTP-based Dynamic Adaptive Streaming (DASH), HTTP Live Streaming (HLS), Real-Time Streaming Protocol (RTSP), HTTP Dynamic Streaming, etc.

[0050] Destination device 116 can access encoded video data from file server 114 via any standard data connection, including an internet connection. This may include a wireless channel (e.g., Wi-Fi connection), a wired connection (e.g., digital subscriber line (DSL), cable modem, etc.), or a combination of both, suitable for accessing encoded video data stored on file server 114. Input interface 122 can be configured to operate according to any or more of the various protocols discussed above for retrieving or receiving media data from file server 114, or other such protocols for retrieving media data.

[0051] Output interface 108 and input interface 122 can represent a wireless transmitter / receiver, a modem, a wired networking component (e.g., an Ethernet card), a wireless communication component operating according to any of the various IEEE 802.11 standards, or other physical components. In examples where output interface 108 and input interface 122 include wireless components, output interface 108 and input interface 122 can be configured to transmit data such as encoded video data according to cellular communication standards such as 4G, 4G-LTE (Long Term Evolution), Advanced LTE, 5G, etc. In some examples where output interface 108 includes a wireless transmitter, output interface 108 and input interface 122 can be configured according to other wireless standards such as the IEEE 802.11 specification, the IEEE 802.15 specification (e.g., ZigBee), etc. ™ ),Bluetooth ™Standards are used to transmit data such as encoded video data. In some examples, source device 102 and / or destination device 116 may include corresponding system-on-chip (SoC) devices. For example, source device 102 may include an SoC device for performing functions belonging to video encoder 200 and / or output interface 108, and destination device 116 may include an SoC device for performing functions belonging to video decoder 300 and / or input interface 122.

[0052] The technology disclosed herein can be applied to video decoding to support any multimedia application in a variety of multimedia applications, such as over-the-air television broadcasting, cable television transmission, satellite television transmission, internet streaming video transmission (such as HTTP-based Dynamic Adaptive Streaming (DASH)), digital video encoded onto data storage media, decoding of digital video stored on data storage media, or other applications.

[0053] The input interface 122 of the destination device 116 receives an encoded video bitstream from a computer-readable medium 110 (e.g., a communication medium, storage device 112, file server 114, etc.). The encoded video bitstream may include signaling information defined by the video encoder 200 and also used by the video decoder 300, such as syntax elements having values ​​describing the characteristics and / or processing of video blocks or other decoded units (e.g., slices, pictures, picture groups, sequences, etc.). The display device 118 displays decoded images of the decoded video data to the user. The display device 118 may represent any of a variety of display devices, such as a liquid crystal display (LCD), a plasma display, an organic light-emitting diode (OLED) display, or another type of display device.

[0054] Despite Figure 1Not shown, but in some examples, both the video encoder 200 and the video decoder 300 may be integrated with the audio encoder and / or audio decoder (e.g., audio codec), and may include appropriate MUX-DEMUX units or other hardware and / or software to process multiplexed streams that include both audio and video in a common data stream. Example audio codecs may include AAC, AC-3, AC-4, ALAC, ALS, AMBE, AMR, AMR-WB (G.722.2), AMR-WB+, aptX (various versions), ATRAC, BroadVoice (BV16, BV32), CELT, Enhanced AC-3 (E-AC-3), EVS, FLAC, G.711, G.722, G.722.1, G.722.2 (AMR-WB), G.723.1, G.726, G.728, G.729, G.729.1, GSM-FR, HE-AAC, iLBC, iSAC, LA Lyra, Monkey's Audio, MP1, MP2 (MPEG-1, 2 Audio Layer II), MP3, Musepack, Nellymoser Asao, OptimFROG, Opus, Sac, Satin, SBC, SILK, Siren 7, Speex, SVOPC, True Audio (TTA), TwinVQ, USAC, Vorbis (Ogg), WavPack and Windows Media Aud.

[0055] Both the video encoder 200 and the video decoder 300 can be implemented as any of a variety of suitable encoder and / or decoder circuits comprising a processing system, such as one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), discrete logic, software, hardware, firmware, or any combination thereof. When the technology is implemented in part in software, the device may store instructions for the software in a suitable non-transitory computer-readable medium and execute the instructions in hardware using one or more processors to perform the technology of this disclosure. Each of the video encoder 200 and the video decoder 300 may be included in one or more encoders or decoders, either of which may be integrated as part of a combined encoder / decoder (CODEC) in the respective device. Devices including the video encoder 200 and / or the video decoder 300 may implement the video encoder 200 and / or the video decoder 300 in processing circuitry such as integrated circuits and / or microprocessors. Such devices may be wireless communication devices (such as cellular phones) or any other type of device described herein.

[0056] The video encoder 200 and video decoder 300 may operate according to a video decoding standard such as ITU-T H.265 (also known as High Efficiency Video Decoding (HEVC)) or extensions thereof such as MultiView and / or Scalable Video Decoding Extensions). Alternatively, the video encoder 200 and video decoder 300 may operate according to other proprietary or industry standards such as ITU-T H.266 (also known as Multi-Functional Video Decoding (VVC)). In other examples, the video encoder 200 and video decoder 300 may operate according to proprietary video codecs / formats such as AOMedia Video 1 (AV1), extensions to AV1, and / or subsequent versions of AV1 (e.g., AV2)). In other examples, the video encoder 200 and video decoder 300 may operate according to other proprietary formats or industry standards. However, the technology disclosed herein is not limited to any particular decoding standard or format. Typically, the video encoder 200 and the video decoder 300 can be configured to perform the techniques of this disclosure in conjunction with any video decoding technique that uses multiple prediction models and signaling in video decoding.

[0057] For example, video encoder 200 and video decoder 300 may utilize various models to generate predictions (e.g., prediction blocks). This disclosure describes example techniques for determining which model to use while reducing signaling overhead. In this way, video encoder 200 and video decoder 300 can support an increase in the number of models available while minimizing the signaling overhead penalty that results from adding more models.

[0058] Typically, video encoder 200 and video decoder 300 perform block-based decoding of images. The term "block" generally refers to a structure that includes data to be processed (e.g., encoded, decoded, or otherwise used during encoding and / or decoding). For example, a block may include a two-dimensional matrix of samples of luminance and / or chrominance data. Typically, video encoder 200 and video decoder 300 decode video data represented in YUV (e.g., Y, Cb, Cr) format. That is, instead of decoding the red, green, and blue (RGB) data used for images, video encoder 200 and video decoder 300 decode the luminance and chrominance components, where the chrominance components may include both red hue chrominance components and blue hue chrominance components. In some examples, video encoder 200 converts the received RGB format data to a YUV representation before encoding, and video decoder 300 converts the YUV representation to RGB format. Alternatively, preprocessing and postprocessing units (not shown) may perform these conversions.

[0059] This disclosure generally relates to the decoding (e.g., encoding and decoding) of images to include processes of encoding or decoding data of the image. Similarly, this disclosure may relate to the decoding of blocks of images to include processes of encoding or decoding data for the blocks (e.g., prediction and / or residual decoding). Encoded video bitstreams typically include a series of values ​​for syntax elements representing decoding decisions (e.g., decoding modes) and the partitioning of images into blocks. Therefore, references to the decoding of images or blocks should generally be understood as the decoded values ​​of the syntax elements that form the images or blocks.

[0060] HEVC defines various blocks, including decoding units (CUs), prediction units (PUs), and transform units (TUs). According to HEVC, a video decoder (such as a video encoder 200) divides the decoding tree unit (CTU) into CUs based on a quadtree structure. That is, the video decoder divides the CTU and CU into four equal, non-overlapping squares, and each node of the quadtree has zero or four child nodes. Nodes without child nodes are called "leaf nodes," and the CU of such leaf nodes may include one or more PUs and / or one or more TUs. The video decoder may further divide the PUs and TUs. For example, in HEVC, a residual quadtree (RQT) represents the partitioning of the TU. In HEVC, the PU represents inter-frame prediction data, while the TU represents residual data. The CU after intra-frame prediction includes intra-frame prediction information, such as intra-frame mode indication.

[0061] As another example, video encoder 200 and video decoder 300 can be configured to operate according to VVC. According to VVC, the video decoder (such as video encoder 200) partitions the image into multiple CTUs. Video encoder 200 can partition the CTUs according to a tree structure (such as a quadtree-binary tree (QTBT) structure or a multi-type tree (MTT) structure). The QTBT structure removes the concept of multiple partition types, such as the separation between CUs, PUs, and TUs in HEVC. The QTBT structure includes two levels: a first level partitioned according to a quadtree and a second level partitioned according to a binary tree. The root node of the QTBT structure corresponds to a CTU. The leaf nodes of the binary tree correspond to CUs.

[0062] In the MTT partitioning structure, blocks can be divided using quadtree (QT) partitioning, binary tree (BT) partitioning, and one or more types of ternary tree (TT) partitioning (also known as triplet tree (TT)). A ternary tree or triplet tree partition is a partition in which a block is divided into three sub-blocks. In some examples, a ternary tree or triplet tree partition divides a block into three sub-blocks without dividing the original block through the center. Partition types in MTT (e.g., QT, BT, and TT) can be symmetric or asymmetric.

[0063] When operating according to the AV1 codec, the video encoder 200 and video decoder 300 can be configured to decode video data in blocks. In AV1, the largest decoded block that can be processed is called a superblock. In AV1, a superblock can be 128x128 luma samples or 64x64 luma samples. However, in subsequent video decoding formats (e.g., AV2), superblocks can be defined by different (e.g., larger) luma sample sizes. In some examples, the superblock is the top level of a block quadtree. The video encoder 200 can further divide the superblock into smaller decoded blocks. The video encoder 200 can use square or non-square partitions to divide the superblock and other decoded blocks into smaller blocks. Non-square blocks can include N / 2xN blocks, NxN / 2 blocks, N / 4xN blocks, and NxN / 4 blocks. The video encoder 200 and video decoder 300 can perform separate prediction and transform processing for each decoded block.

[0064] AV1 also defines video data tiles. A tile is a rectangular array of superblocks that can be decoded independently of other tiles. That is, the video encoder 200 and video decoder 300 can encode and decode the decoding blocks within a tile separately without using video data from other tiles. However, the video encoder 200 and video decoder 300 can perform filtering across tile boundaries. The tile size can be uniform or non-uniform. Tile-based decoding enables parallel processing and / or multithreading in the encoder and decoder implementations.

[0065] In some examples, the video encoder 200 and the video decoder 300 may use a single QTBT or MTT structure to represent each of the luma and chroma components, while in other examples, the video encoder 200 and the video decoder 300 may use two or more QTBT or MTT structures, such as one QTBT / MTT structure for the luma component and another QTBT / MTT structure for the two chroma components (or two QTBT / MTT structures for the respective chroma components).

[0066] The video encoder 200 and the video decoder 300 can be configured to use quadtree partitioning, QTBT partitioning, MTT partitioning, superblock partitioning or other partitioning structures.

[0067] In some examples, a CTU includes a decoded tree block (CTB) of luminance samples, two corresponding CTBs of chrominance samples of an image with three sample arrays, or a CTB of samples of an image decoded using three separate color planes and a syntax structure for decoding the samples. A CTB can be an NxN sample block of some value N, such that a partitioning method divides the components into CTBs. A component is an array or a single sample from one of the three arrays (luminance and two chrominance) constituting a 4:2:0, 4:2:2, or 4:4:4 color format image, or an array or a single sample constituting an array or array constituting a monochrome format image. In some examples, a decoded block is an MxN sample block of values ​​M and N, such that a partitioning method divides the CTB into decoded blocks.

[0068] Blocks (e.g., CTUs or CUs) can be grouped in various ways within an image. As an example, a brick can refer to a rectangular area of ​​a row of CTUs within a specific tile in an image. A tile can be a rectangular area of ​​CTUs within a specific tile column and a specific tile row in an image. A tile column refers to a rectangular area of ​​a CTU having a height equal to the height of the image and a width specified by syntax elements (e.g., such as in an image parameter set). A tile row refers to a rectangular area of ​​a CTU having a height specified by syntax elements (e.g., such as in an image parameter set) and a width equal to the width of the image.

[0069] In some examples, a tile can be divided into multiple bricks, each brick comprising one or more CTU rows within the tile. A tile that is not divided into multiple bricks can also be called a brick. However, bricks that are a true subset of a tile cannot be called a tile. Bricks in an image can also be arranged in slices. A slice can be an integer number of bricks in an image that can be uniquely contained within a single Network Abstraction Layer (NAL) unit. In some examples, a slice comprises multiple complete tiles or a consecutive sequence of complete bricks comprising only one tile.

[0070] This disclosure uses "NxN" and "N by N" interchangeably to refer to the sample size of a block (such as a CU or other video block) in the vertical and horizontal dimensions, for example, 16x16 samples or 16 by 16 samples. Typically, a 16x16 CU will have 16 samples in the vertical direction (y=16) and 16 samples in the horizontal direction (x=16). Similarly, an NxN CU typically has N samples in the vertical direction and N samples in the horizontal direction, where N represents a non-negative integer value. Samples in a CU may be arranged in rows and columns. Furthermore, a CU does not necessarily need to have the same number of samples in the horizontal direction as it does in the vertical direction. For example, a CU may include NxM samples, where M is not necessarily equal to N.

[0071] The video encoder 200 encodes video data representing prediction and / or residual information, as well as other information, for use in the control unit (CU). The prediction information indicates how the CU should be predicted to form a prediction block for the CU. The residual information typically represents the sample-by-sample difference between a sample of the CU before encoding and the prediction block.

[0072] To predict the Cubic Frame (CU), the video encoder 200 typically forms prediction blocks for the CU through inter-frame prediction or intra-frame prediction. Inter-frame prediction typically refers to predicting the CU from data in a previously decoded image, while intra-frame prediction typically refers to predicting the CU from data in a previously decoded image within the same frame. To perform inter-frame prediction, the video encoder 200 can use one or more motion vectors to generate prediction blocks. The video encoder 200 can typically perform a motion search to identify reference blocks that closely match the CU, for example, based on the differences between the CU and a reference block. The video encoder 200 can use sum of absolute differences (SAD), sum of squared differences (SSD), mean absolute difference (MAD), mean squared difference (MSD), or other such difference calculations to compute difference metrics to determine whether a reference block closely matches the current CU. In some examples, the video encoder 200 can use unidirectional or bidirectional prediction to predict the current CU.

[0073] Some examples of VVC also provide an affine motion compensation mode, which can be viewed as an inter-frame prediction mode. In affine motion compensation mode, the video encoder 200 can determine two or more motion vectors representing non-translational motion, such as zooming in or out, rotation, perspective motion, or other irregular motion types.

[0074] To perform intra-frame prediction, the video encoder 200 can select an intra-frame prediction mode to generate prediction blocks. Some examples of VVC provide sixty-seven intra-frame prediction modes, including various directional modes, as well as planar and DC modes. Typically, the video encoder 200 selects an intra-frame prediction mode that describes the neighboring samples of the current block (e.g., the block of the CU), from which it predicts samples for the current block. Assuming the video encoder 200 decodes the CTU and CU in raster scan order (from left to right, from top to bottom), such samples are typically located above, to the upper left, or to the left of the current block within the same frame as the current block.

[0075] The video encoder 200 encodes data representing the prediction mode of the current block. For example, for inter-frame prediction modes, the video encoder 200 may encode data indicating which of the various available inter-frame prediction modes is used, as well as the motion information for the corresponding mode. For example, for unidirectional or bidirectional inter-frame prediction, the video encoder 200 may encode motion vectors using Advanced Motion Vector Prediction (AMVP) or merging modes. The video encoder 200 may use similar modes to encode motion vectors used for affine motion compensation modes.

[0076] AV1 includes two common techniques for encoding and decoding blocks of video data. These two common techniques are intra-frame prediction (e.g., intra-frame prediction or spatial prediction) and inter-frame prediction (e.g., inter-frame prediction or temporal prediction). In the context of AV1, when using intra-frame prediction modes to predict blocks of video data for the current frame, the video encoder 200 and video decoder 300 do not use video data from other frames of the video data. For most intra-frame prediction modes, the video encoder 200 encodes blocks of the current frame based on the difference between sample values ​​in the current block and predicted values ​​generated from reference samples in the same frame. The video encoder 200 determines the predicted values ​​generated from the reference samples based on the intra-frame prediction mode.

[0077] After prediction (such as intra-frame or inter-frame prediction for a block), the video encoder 200 can compute residual data for the block. The residual data (such as a residual block) represents the sample-by-sample difference between the block and the prediction block used to form the block, which is formed using the corresponding prediction mode. The video encoder 200 can apply one or more transforms to the residual block to produce transformed data in the transform domain rather than the sample domain. For example, the video encoder 200 can apply a Discrete Cosine Transform (DCT), an integer transform, a wavelet transform, or a conceptually similar transform to the residual video data. Additionally, the video encoder 200 can apply a secondary transform after the first transform, such as the Mode Correlated Inseparable Secondary Transform (MDNSST), the Signal Correlation Transform, the Karhunen-Loeve Transform (KLT), etc. The video encoder 200 produces transform coefficients after applying one or more transforms.

[0078] As noted above, after any transform that produces the transform coefficients, the video encoder 200 may perform quantization on the transform coefficients. Quantization generally refers to the process in which the transform coefficients are quantized to reduce the amount of data used to represent them, thereby providing further compression. By performing the quantization process, the video encoder 200 may reduce the bit depth associated with some or all of the transform coefficients. For example, the video encoder 200 may quantize the transform coefficients during quantization. n The place value is rounded down to... m Bit value, where n Greater than m In some examples, in order to perform quantization, the video encoder 200 may perform a bitwise right shift on the value to be quantized.

[0079] After quantization, the video encoder 200 can scan the transform coefficients to generate a one-dimensional vector from a two-dimensional matrix containing the quantized transform coefficients. The scan can be designed to place higher-energy (and therefore lower-frequency) transform coefficients before the vector and lower-energy (and therefore higher-frequency) transform coefficients after the vector. In some examples, the video encoder 200 can utilize a predefined scan order to scan the quantized transform coefficients to produce a serialized vector, and then entropy-encode the quantized transform coefficients of that vector. In other examples, the video encoder 200 can perform an adaptive scan. After scanning the quantized transform coefficients to form a one-dimensional vector, the video encoder 200 can entropy-encode the one-dimensional vector, for example, according to context-adaptive binary arithmetic decoding (CABAC). The video encoder 200 can also entropy-encode the values ​​of syntax elements describing metadata associated with the encoded video data, which is used by the video decoder 300 when decoding the video data.

[0080] To perform CABAC, the video encoder 200 can assign context within a context model to the symbols to be transmitted. Context may involve, for example, whether the neighboring values ​​of a symbol are zero. Probability determination can be based on the context assigned to the symbols.

[0081] The video encoder 200 may further generate syntax data for the video decoder 300, such as block-based syntax data, image-based syntax data, and sequence-based syntax data, for example, in image headers, block headers, and slice headers, or generate other syntax data such as sequence parameter sets (SPS), image parameter sets (PPS), or video parameter sets (VPS). The video decoder 300 may also decode such syntax data to determine how to decode the corresponding video data.

[0082] In this way, the video encoder 200 can generate a bitstream that includes encoded video data, such as syntax elements describing the partitioning of images into blocks (e.g., CUs) and prediction and / or residual information for the blocks. Finally, the video decoder 300 can receive the bitstream and decode the encoded video data.

[0083] Typically, the video decoder 300 performs the reverse process of the video encoder 200 to decode the encoded video data of the bitstream. For example, the video decoder 300 can use CABAC to decode the values ​​of syntax elements used for the bitstream in a manner substantially similar to but reversed by the CABAC encoding process of the video encoder 200. Syntax elements can define partitioning information for dividing a picture into CTUs and defining the CUs of each CTU according to a corresponding partitioning structure such as a QTBT structure. Syntax elements can further define prediction and residual information for video data blocks (e.g., CUs).

[0084] The residual information can be represented, for example, by quantized transform coefficients. The video decoder 300 can inversely quantize and inverse transform the quantized transform coefficients of the block to reconstruct the residual block for the block. The video decoder 300 uses a signaling prediction mode (intra-frame prediction or inter-frame prediction) and associated prediction information (e.g., motion information for inter-frame prediction) to form a prediction block for the block. The video decoder 300 can then combine the prediction block and the residual block (on a sample-by-sample basis) to reconstruct the original block. The video decoder 300 can perform additional processing, such as performing a deblocking process to reduce visual artifacts along the block boundaries.

[0085] This disclosure may generally relate to "signaling" certain information (such as syntax elements). The term "signaling" can generally refer to the communication of values ​​and / or other data of syntax elements used to decode encoded video data. That is, video encoder 200 may signal the values ​​of syntax elements in the bitstream. Generally, signaling refers to generating values ​​in the bitstream. As noted above, source device 102 may transmit the bitstream to destination device 116 substantially in real time or not in real time (such as when syntax elements are stored in storage device 112 for later retrieval by destination device 116).

[0086] According to the technology disclosed herein, there are various tools that utilize model derivation at the video encoder 200 and video decoder 300 to form predictions. Examples of such tools are Local Intensity Compensation (LIC), Linear Model (LM), and Convolutional Cross Component Model (CCCM), where the model is derived by using reconstructed neighboring luma and chroma data to derive the cross component model or by using neighboring samples and neighboring samples of the reference block to derive a model for inter-frame prediction.

[0087] As described above, as used in this disclosure, "model" refers to an encoding or decoding technique in which video encoder 200 and video decoder 300 apply model parameters (e.g., scaling and offset) to sample values ​​to generate a predicted block for the current block being encoded or decoded. The sample values ​​may be for previously reconstructed samples, and the model parameters may be applied to a portion of the pixel domain (e.g., from the perspective of the video encoder before transform or quantization and from the perspective of the video decoder after inverse quantization and inverse transform). The video encoder and video decoder may determine the model parameters based on previously reconstructed samples. In some examples, the video encoder may transmit a signal, and the video decoder may receive the model parameters. For example, for model-based decoding, video encoder 200 and video decoder 300 may determine the model parameters based on previously reconstructed samples, determine the samples to which the model parameters are applied (the model parameters may be based on previously reconstructed samples), and apply the model parameters (e.g., perform scaling and / or offset using the model parameters) to the determined samples to generate a predicted block for the current block.

[0088] As described in more detail, the example technique involves video encoder 200 and video decoder 300 determining the same model for encoding or decoding the current block in a manner that reduces signaling overhead. For example, the example technique can reduce the signaling overhead required to select a model from multiple existing models. Furthermore, as additional models become available, including such models for video decoding, it may be possible without adding excessive signaling overhead. Additionally, the example technique is applicable to the current block that is encoded or decoded using inter-frame prediction or intra-frame prediction.

[0089] One way to indicate the model is by sending syntax elements via signaling in the bitstream. However, when the number of models is large, the signaling overhead can be relatively high. As noted above, this disclosure describes an example of determining which model to use while reducing signaling overhead.

[0090] The following describes some examples of models used in video decoding. These example techniques should not be considered as limiting to any particular example model.

[0091] For the Convolutional Cross-Component Model (CCCM), in JVET-Z0064 (Lainema et al., “AHG12: Convolutional cross-component model (CCCM) for intra prediction” Joint VideoExperts Team (JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC 1 / SC 29, 26) thmeeting, by teleconference, 20-29 April 2022) and JVET-AA0057 (Astola et al., “EE2-1.1a: Convolutional cross-component intra prediction model” Joint Video ExpertsTeam (JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC 1 / SC 29, 27 th At the meeting, byteleconference, July 13-22, 2022, a method was proposed to apply the Convolutional Cross Component Model (CCCM) to predict chroma samples from reconstructed luminance samples using a technique similar to the Cross Component Linear Model (CCLM). As with CCLM, when chroma subsampling is used, the reconstructed luminance samples are downsampled to match a lower-resolution chroma grid.

[0092] In addition, similar to CCLM, there are options for using a single model or a multi-model variant of CCCM. The multi-model variant uses two models, one derived for samples above the average luminance reference value and the other for the remaining samples (following the spirit of the CCLM design). For PUs with at least 128 available reference samples, the multi-model CCCM mode can be selected.

[0093] The 7-tap convolutional filter used consists of a 5-tap plus shape spatial component, a nonlinear term, and a bias term. The input to the spatial 5-tap component of filter 600 consists of a center (C) luminance sample co-located with the predicted chroma sample and the chroma samples adjacent to its top / north (N), bottom / south (S), left / west (W), and right / east (E) sides, as shown below. Figure 6 exemplified.

[0094] If the color format is not 4:4:4, where the number of chroma samples is less than the number of luminance samples, then the luminance samples described above are downsampled luminance samples. The non-linear term P is represented as a power of two of the center luminance sample C, scaled proportionally to the range of sample values ​​in the content: P = (C*C + midVal) >> bitdepth. That is, for 10-bit content, it is calculated as: P = (C*C + 512) >> 10.

[0095] The bias term B represents the scalar offset between the input and output (similar to the offset term in CCLM) and is set to an intermediate chroma value (512 for 10-bit content). The filter output is calculated as the filter coefficients c. iConvolution with the input values ​​and cropped to the range of valid chromaticity samples: predChromaVal = c0C + c1N + c2S + c3E + c4W + c5P + c6B.

[0096] The filter coefficients c are calculated by minimizing the mean square error (MSE) between the predicted chromaticity samples and the reconstructed chromaticity samples in the reference region. i Error! Reference source not found. Figure 7 A reference region 702 is illustrated, consisting of six rows of chromaticity samples above and to the left of PU 700. Reference region 702 extends to the right by one PU width and below the PU boundary by one PU height. This region can be adjusted to include only usable samples. Expansion of regions as indicated by reference numerals 704A to 704F may be necessary to support the “side samples” of the plus-shaped spatial filter and to fill areas that are not usable.

[0097] MSE minimization is performed by calculating the autocorrelation matrix of the luminance input and the cross-correlation vector between the luminance input and chrominance output. The autocorrelation matrix is ​​decomposed using LDL, and the final filter coefficients are computed using back-substitution. This process roughly follows the calculation of ALF (Adaptive Loop Filter) filter coefficients in ECM, but LDL decomposition is chosen instead of Cholesky decomposition to avoid using square root operations. The example method described above uses only integer operations on the model.

[0098] In JVET-AB0180 (Jhu et al., “Non-EE2: CCCM using non-downsampled lumasamples” Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC1 / SC 29, 28) th meeting, Mainz, DE, 20-28 October 2022) and JVET-AB0187 (Sereginet al., “Non-EE2: No luma subsampling for CCCM” Joint Video Experts Team(JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC 1 / SC 29, 28 thIn the meeting, Mainz, DE, 20-28 October 2022, a method was proposed to predict chroma samples from reconstructed luminance samples using a CCCM without downsampling of luminance samples and without luminance subsampling for the CCCM (i.e., no downsampling). In the case of a typical 4:2:0 color format, this (e.g., the example technique of JVET-AB0187) consists of a 6-tap spatial term, four nonlinear terms, and a bias term. The 6-tap spatial term corresponds to the 6 neighboring luminance samples (i.e., C) around the predicted chroma sample (i.e., C). L 0 , L 1 , …… , L 5 ), four nonlinear terms L 0 , L 1 , L 2 and L 3 It was derived from the sample.

[0099] The equation for C is as follows: in It is a coefficient. It's an offset. If the coordinates of the chroma sample are (x, y), then... The coordinates of (i = 0, 1, ..., 5) are (2x-1, 2y), (2x, 2y), (2x+1, 2y), (2x-1, 2y+1), (2x, 2y+1), and (2x+1, 2y+1).

[0100] The following describes gradient- and location-based CCCM (GL-CCCM). The GL-CCCM method uses gradient and location information, rather than the four spatially neighboring samples in the CCCM filter. The GL-CCCM filter used for prediction is: predChromaVal = c0C + c1G y + c2G x + c3Y + c4X + c5P + c6B, where G y and G x These are the vertical and horizontal gradients, respectively, and are calculated as follows: G y = (2N + NW + NE) - (2S + SW + SE) G x = (2W + NW + SW) - (2E + NE + SE)

[0101] Furthermore, the Y and X parameters represent the vertical and horizontal positions of the center brightness sample. The remaining parameters are the same as those in the CCCM tool. The reference area for parameter calculation is the same as that in the CCCM method. Figure 8 This is a conceptual diagram illustrating the GL-CCCM filter 800.

[0102] The following describes the cross-component residual model used for inter-frame prediction. This is based on JVET-AD0108 (Huang et al., “Non-EE2: Affine AMVP mode with one MVD” Joint Video Experts Team (JVET) of ITU-TSG 16 WP 3 and ISO / IEC JTC 1 / SC 29, 30). th In the meeting (Antalya, TR, 21-28 April 2023), a cross-component residual model (CCRM) was proposed to predict chroma samples from reconstructed luminance samples when inter-frame prediction or intra-block copying (IBC) is used for blocks. Cross-component filters are derived using the predicted luminance and chrominance signals. The derived filters are then applied to the reconstructed luminance signal to generate the final chrominance prediction.

[0103] The proposed 8-tap filter consists of 6 spatial brightness samples, a nonlinear term, and a bias term. For example... Figure 9 As shown, spatial luminance samples (L0, ..., L5) are obtained from a luminance grid by selecting the 6 luminance samples closest to the chromaticity position C without downsampling. The predicted chromaticity value is obtained as: predChromaVal = c0L0 + c1L1 + c2L2 + c3L3 + c4L4 + c5L5 + c6nonlinear((L0 + L3 + 1) >> 1) + c7B, where nonlinear is the nonlinear operator of CCCM and B is the bias.

[0104] The following describes the direct block vector mode used for chroma prediction. This is described in JVET-AC0071 (Huo et al., “EE2-3.1: Direct block vector mode for chroma prediction” Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC 1 / SC 29, 29). thIn the meeting, byteleconference, 11-20 January 2023, a direct block vector was proposed to improve the decoding efficiency of chroma components when initiating dual-tree decoding in intra-frame slices. The method in JVET-AC0071 includes two specific implementations. When initiating dual-tree decoding in intra-frame slices, for chroma CUs decoded to DBV mode, if IBC mode or IntraTmp mode is used for the five positions (e.g., ... Figure 10 If we decode one of the luminance blocks in the luminance block (i.e., one of the five positions in the reconstructed luminance sample) as shown in luminance component 1000, then its block vector bvL is used to derive the chrominance block vector bvC. Figure 10 The chromaticity component 1002 is also shown as an example.

[0105] The following describes block vector guided CCCM. In JVET-AD0100 (Youvalari et al., “AHG12: Blockvector guided CCCM” Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC 1 / SC 29, 30) th In a meeting (Antalya, TR, 21-28 April 2023), a Block Vector Guided CCCM (BVG-CCCM) method was proposed to improve the decoding efficiency of ECM. The BVG-CCCM method uses the block vector of the co-located luma block decoded in IBC or intraTMP mode to determine the reference region for calculating CCCM parameters. Then, the reference region in the luma and the corresponding region in the chroma channel are used to calculate the CCCM parameters. This prediction uses the calculated model parameters and co-located luma samples to perform CCCM prediction. Figure 11A and Figure 11B A reference region for the BVG-CCCM method is illustrated. For example, Figure 11A An example is given of a reference brightness 1102 used for a common brightness 1100, and... Figure 11B The reference chromaticity 1106 used for the current block 1104 is shown as an example.

[0106] Similar to the Direct Block Vector (DBV) mode in ECM-8.0, five positions in the co-location luminance block region are scanned to determine the block vector to be used in the BVG-CCCM method. This mode uses PU level flags decoded by CABAC, which are then transmitted via signaling. If the co-location block is decoded in IBC or intraTMP mode and the cross component index is LM_CHROMA_IDX or MMLM_CHROMA_IDX, the BVG-CCCM flag is transmitted via signaling.

[0107] The following describes the cross-component merging mode for chroma intra-frame decoding. This is described in JVET-AC0315 (Tsai et al., “Non-EE2: Cross-component merge mode for chroma intra coding” Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC 1 / SC 29, 29). th In the meeting, byteleconference, 11-20 January 2023, the Cross Component Merging (CCMerge) mode for chroma intra-frame decoding was introduced, which includes cross component model parameters inherited by the current chroma block from its spatially adjacent and non-adjacent neighbors or the default model. Spatially adjacent and non-adjacent neighbor information is collected from blocks previously decoded using CCLM, MMLM, CCCM, GLM, chroma fusion, and CCMerge modes. The final cross component model parameters of the current chroma block can be inherited from its spatially adjacent and non-adjacent neighbors or the default model. A list is created that includes CCP models from spatially adjacent and non-adjacent neighbors decoded using CCLM, MMLM, CCCM, GLM, chroma fusion, and CCMerge modes. After including adjacent CCP models, the default model is also included to fill any remaining spaces in the list. To avoid including redundant CCP models in the list, a pruning operation is applied.

[0108] The following describes the reduction in the dynamic range of the convolutional cross-component model. JVET-AA0114 (Aminlou et al., “EE2-related: Division-free operation and mean-compensation for convolutional cross-component model (CCCM)” Joint Video Experts Team (JVET) of ITU-T SG 16WP 3 and ISO / IEC JTC 1 / SC 29, 27) th(Meeting, by teleconference, 13-22 July 2022) proposed simplifying the implementation of CCCM by removing the mean of the reference sample from the sample values ​​and replacing the division operation with a piecewise polynomial function. Division removal is considered desirable, but mean removal is considered undesirable because it introduces additional pipeline stages. In JVET-AB0174 (Aminlou et al., “AHG12: Division-free operation and dynamic range reduction for convolutional cross-component model (CCCM)” Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO / IEC JTC1 / SC 29, 28) th In the meeting, Mainz, DE, 20-28 October 2022, mean removal was replaced by offset removal, which does not add any additional pipeline stages or additional sample-level operations. JVET-AB0174 proposed removing a fixed offset from the luminance and chrominance samples in each PU of each model, which could result in a reduction in the magnitude of the values ​​used in model creation and allow for a reduction in the precision required for fixed-point operations. Therefore, JVET-AB0174 proposes to use 16-bit decimal precision instead of the 22-bit precision of the original CCCM implementation.

[0109] For simplicity, reference sample values ​​outside the top-left corner of the PU are used as offsets (offsetLuma, offsetCb, and offsetCr). These fixed values ​​reduce the sample values ​​used in both model creation and final prediction (i.e., the luminance and chrominance in the reference region and the luminance in the current PU), as follows: C' = C - offsetLuma N' = N - offsetLuma S' = S - offsetLuma E' = E - offsetLuma W' = W - offsetLuma P' = nonLinear(C') B = midValue = 1<<(bitDepth - 1) Furthermore, the chromaticity values ​​are used for prediction using the following equation, where offsetChroma is equal to the offsetCr and offsetCb of the Cr and Cb components, respectively: predChromaVal = c0C' + c1N' + c2S' + c3E' + c4W' + c5P' + c6B +offsetChroma

[0110] To avoid any additional sample-level operations, the luminance offset is removed during luminance reference sample interpolation. This can be done, for example, by replacing the rounding term used in luminance reference sample interpolation with an updated offset that includes both the rounding term and offsetLuma. The chromaticity offset can be eliminated by directly subtracting it from the reference chromaticity sample. Alternatively, the effect of the chromaticity offset can be removed from the cross-component vector, which gives the same result. To add the chromaticity offset back to the output of the convolution prediction operation, it is added to the bias term of the convolution model. With this option, the convolution operation still requires exactly the same amount of computation as the original implementation of CCCM.

[0111] As illustrated in the examples above, multiple models can exist. As described above, "model" can refer to video decoding techniques in which model parameters (e.g., scaling or offset parameters) are determined based on previously reconstructed samples, and the model parameters are applied to samples determined according to the pixel domain to generate a predicted block for the current block. The techniques used to determine the model parameters, the techniques used to determine the samples to which the model parameters are applied, and / or the techniques used to apply the model parameters to the samples can differ for different models.

[0112] Therefore, in some techniques, the video encoder 200 sends information indicating the model used to decode the current block to the video decoder 300 via signaling. One way to indicate the model is to send syntax elements via signaling in the bitstream. However, when the number of models is large, the signaling overhead can be relatively high. The following describes examples that can address the signaling overhead problem by including various models. The following example techniques can be implemented independently or in any combination.

[0113] In some examples, the video encoder 200 and video decoder 300 may use a cost function to generate a cost metric (i.e., cost value) for each model and use that cost value to rank those multiple models. Then, in one example, the video encoder 200 may signal an index to an ordered list of candidate models, or the video encoder 200 and video decoder 300 may select the model with the lowest cost.

[0114] To derive costs, the derived model is applied to the samples used for model derivation. The difference between the samples after model application and the neighboring samples of the current block indicates model error, which can be used as a cost metric. In another example, the derived model is applied to samples not used in model derivation. The difference between the target sample and the samples derived after model derivation can then be used as a cost metric (e.g., sum of absolute differences), optionally normalized by the number of samples involved in cost derivation.

[0115] In other words, it is assumed that the current block will be encoded or decoded. For each of the multiple models, the video encoder 200 and the video decoder 300 may use a first set of samples to derive the corresponding model parameters associated with the corresponding model among the multiple models. The first set of samples may include samples not immediately adjacent to the current block, or may include samples not immediately adjacent to the current block and samples not immediately adjacent to a reference block. From the perspective of the video encoder 200, the first set of samples may be previously encoded samples that have been reconstructed by the decoder loop (e.g., reconstruction loop) of the video encoder 200. From the perspective of the video decoder 300, the first set of samples may be previously decoded samples. That is, both the video encoder 200 and the video decoder 300 have values ​​from the first set of samples. The first set of samples may be chroma samples, luma samples, or both chroma samples and luma samples.

[0116] For example, multiple models may include Local Intensity Compensation (LIC), Linear Model (LM), Convolutional Cross Component Model (CCCM), Gradient-and-Location-Based CCCM (GL-CCCM), Cross Component Residual Model (CCRM), Block Vector Guided CCCM (BVG-CCCM), and Cross Component Merge (CCMerge). In some examples, at least one of the multiple models is a model used to encode or decode temporally or spatially adjacent blocks of the current block.

[0117] Each of these models can define a specific way to derive the corresponding model parameters. The video encoder 200 and video decoder 300 can input a first set of samples to derive the model parameters for each model in the defined manner. For example, the video encoder 200 and video decoder 300 can input a first set of samples to determine the model parameters of LIC, input a first set of samples to determine the model parameters of LM, and so on.

[0118] It should be noted that the first set of samples does not need to be the same for the model parameters of each model in the derivation model. For example, one model may require one set of samples to determine the model parameters, while another model may require a different set of samples. Therefore, in this disclosure, using the first set of samples to derive the corresponding model parameters associated with the corresponding corresponding model among the multiple models for each model may mean that for each model among the multiple models, the corresponding model parameters associated with the corresponding corresponding model among the multiple models are derived using the samples used to derive the model parameters for that particular model. However, the first set of samples may also be the same for each model.

[0119] The video encoder 200 and video decoder 300 can use corresponding model parameters and a second set of samples to determine the corresponding cost values ​​associated with the corresponding model among a plurality of models, thereby generating the corresponding cost values ​​for the plurality of models. In some examples, the first set of samples and the second set of samples may be the same set of samples or may partially overlap.

[0120] In some examples, the first and second sets of samples are mutually exclusive. That is, no sample from the first set is included in the second set, and vice versa. Similarly, it should be noted that the second set of samples does not need to be the same for each model in the model. However, the second set of samples may also be the same for each model.

[0121] For example, if the first set of samples and the second set of samples are mutually exclusive, the second set of samples may include samples immediately adjacent to the current block, and the first set of samples may include samples not immediately adjacent to the current block. As another example, if the first set of samples and the second set of samples are mutually exclusive, the second set of samples includes samples immediately adjacent to the current block and samples immediately adjacent to the reference block, and the first set of samples includes samples not immediately adjacent to the current block and samples not immediately adjacent to the reference block.

[0122] To determine the corresponding cost value, the video encoder 200 and video decoder 300 can generate prediction samples for a second set of samples based on model parameters for each of the multiple models. That is, the video encoder 200 and video decoder 300 can perform operations on the second set of samples as if the second set of samples were being predicted. As described above, as part of model-based decoding, the video encoder 200 and video decoder 300 generate prediction samples for blocks being encoded or decoded. In one or more examples, the video encoder 200 and video decoder 300 can utilize the second set of samples as one or more blocks to be encoded or decoded. Similarly, the second set of samples has already been encoded or decoded, but for model selection purposes (e.g., determining the model used to encode or decode the current block), the video encoder 200 and video decoder 300 can determine prediction samples for the second set of samples that are consistent with the technique used for each of the models.

[0123] Since the second set of samples is known, the video encoder 200 and video decoder 300 can determine the differences between the predicted samples and the second set of samples (e.g., SAD or some other technique). The video encoder 200 and video decoder 300 can then determine the cost value associated with the model based on the determined differences. As an example, the video encoder 200 and video decoder 300 can determine the SAD value of each model in the model as the cost value.

[0124] For example, to determine a corresponding cost value, the video encoder 200 and the video decoder 300 may determine a first predicted signal associated with a first model based on model parameters of a first model among multiple models, and determine a first cost value among the corresponding cost values ​​based on the difference between the first predicted signal and a second set of samples. Similarly, the video encoder 200 and the video decoder 300 may determine a second predicted signal associated with a second model based on model parameters of a second model among multiple models, and determine a second cost value among the corresponding cost values ​​based on the difference between the second predicted signal and a second set of samples, and so on.

[0125] In some examples, to determine a model, video encoder 200 and video decoder 300 may select the model associated with the lowest cost value among the corresponding cost values. In some examples, video encoder 200 and video decoder 300 may construct a list of candidate models based on the corresponding cost values ​​(e.g., sorted from lowest cost to highest cost). That is, video encoder 200 and video decoder 300 may construct a list of candidate models where candidates are sorted in the model list based on the corresponding cost values. To determine a model, video encoder 200 and video decoder 300 may select a model from the list of candidate models. Video encoder 200 may transmit an index of the list of candidate models via a signal, and video decoder 300 may parse the index of the list of candidate models.

[0126] As an example, for illustrative purposes, the LIC tool is used. A typical LIC model is a linear model used to compensate for local illumination variations, which can be formulated as pred = a * sample + b, where pred is the final predictor, sample is the motion-compensated sample, and a and b are the linear model parameters derived from the reconstructed neighboring samples of the current block and the neighboring samples of the reference block indicated by the motion vector. The linear least-squares minimization process can be used to derive the model parameters.

[0127] In LIC extensions, several LIC models can exist, such as those using only the left template in addition to using both the left and top templates simultaneously, or those using only the top template. In other examples, additional LIC models can have different spatial templates, such as 3x3 crosses, 3x3 rhombuses, etc. They can also include various nonlinear terms and biases.

[0128] The multiple models are derived from reconstructed neighboring samples of the current block and a reference block, and can be performed using a similar linear minimization process. These models are then applied to reconstructed neighboring reference samples, and the differences are derived using samples after model application and reconstructed neighboring samples of the current block. The absolute differences can be used as a cost metric.

[0129] In some examples, some neighboring samples of the current block and the reference block may be preserved and not used for model derivation, but such samples are used for model validation to generate models such as... Figure 12 The cost metrics shown. For example, Figure 12 The current block 1200 and reference block 1202 are illustrated. Samples 1204 and 1206 can be used to validate the model, and samples 1208 and 1210 can be used to derive the model.

[0130] exist Figure 12 In this model, neighboring samples 1208 of the current block 1200 and neighboring samples 1210 of the reference block 1202 are used to derive the model, while reserved samples not used in the model derivation (e.g., samples 1204 and 1206) are used to validate the model and derive the cost. To derive the cost, in one example, the model is applied to sample 1206 of the reference block 1202, and the difference between the sample after model application and sample 1204 of the current block 1200 is calculated. The sum of absolute differences (optionally normalized by the number of samples involved) can then be used as a cost metric.

[0131] The above provides some examples of samples used for model derivation and / or validation. However, as an alternative or supplement to the examples above, one or more other lines (not necessarily the closest to the block) can be retained and used for model derivation.

[0132] For example, for each of the multiple models, the video encoder 200 and the video decoder 300 may utilize a first set of samples 1208 and / or 1210 to derive the corresponding model parameters associated with the corresponding model among the multiple models. For example, the first set of samples may include samples not immediately adjacent to the current block 1200, such as sample 1208. The first set of samples may include samples not immediately adjacent to the current block 1200 (such as sample 1208) and samples not immediately adjacent to the reference block 1202 (such as sample 1210). Similarly, techniques may be used to derive which samples within sample 1208 or 1210 are based on specific model parameters (e.g., scaling and offset parameters).

[0133] For CCLM, the model parameters can be a nonlinear term P and a bias term B. The model parameters can be a nonlinear term derived from samples L0 to L3 and an offset used for CCLM with unsampled luminance samples. Vertical and horizontal gradients, along with the nonlinear term P and the bias term B, can be examples of model parameters for GL-CCCM. These are some examples of model parameters, and similar other model parameters may exist for different models.

[0134] Video encoder 200 and video decoder 300 may use corresponding model parameters and a second set of samples to determine the corresponding cost values ​​associated with a corresponding model among a plurality of models, thereby generating the corresponding cost values ​​for the plurality of models. In some examples, the first set of samples and the second set of samples are mutually exclusive. For example, the second set of samples may be sample 1204 and / or sample 1206. The second set of samples may include samples immediately adjacent to the current block 1200 (such as sample 1204). The second set of samples may include samples immediately adjacent to the current block 1200 (such as sample 1204) and samples immediately adjacent to the reference block 1202 (such as sample 1206).

[0135] There are multiple ways to determine the corresponding cost value. As an example, for each of multiple models, video encoder 200 and video decoder 300 can generate predicted samples for a second set of samples based on model parameters. As noted above, video encoder 200 and video decoder 300 can perform operations such as encoding or decoding the second set of samples, like generating predicted samples for the second set of samples. However, the second set of samples is known, and therefore, the encoding and decoding process is for model selection. Video encoder 200 and video decoder 300 can determine the difference between the predicted samples and the second set of samples, and determine the cost value associated with the model based on the determined difference.

[0136] For example, video encoder 200 and video decoder 300 may determine a first predicted signal associated with a first model based on model parameters of a first model among multiple models, and determine a first cost value among corresponding cost values ​​based on the difference between the first predicted signal and a second set of samples. Video encoder 200 and video decoder 300 may determine a second predicted signal associated with a second model based on model parameters of a second model among multiple models, and determine a second cost value among corresponding cost values ​​based on the difference between the second predicted signal and a second set of samples. Video encoder 200 and video decoder 300 may repeat these operations to determine the corresponding cost values ​​for all multiple models.

[0137] Video encoder 200 and video decoder 300 can determine, based on corresponding cost values, which model from a plurality of models will be used to encode or decode the current block 1200. As an example, video encoder 200 and video decoder 300 can select the model associated with the lowest cost value among the corresponding cost values ​​to determine the model. As another example, video encoder 200 and video decoder 300 can construct a list of candidate models based on corresponding cost values ​​(e.g., sorting models from lowest to highest cost). That is, video encoder 200 and video decoder 300 can construct a list of candidate models where candidates are sorted in the model list based on corresponding cost values. To determine a model, video encoder 200 and video decoder 300 can select a model from the list of candidate models. For example, video encoder 200 can signal information indicating the index of the candidate model list, and video decoder 300 can parse the information indicating the index of the candidate model list. Typically, sending smaller values ​​via signaling requires fewer signaling bits, and sorting models from lowest cost (e.g., higher probability of use) to highest cost (e.g., lower probability of use) means that the video encoder 200 may send relatively small values ​​via signaling, which increases the likelihood of reducing the amount of signaling bits.

[0138] Video encoder 200 and video decoder 300 can encode or decode the current block 1200 based on a determined model. As an example, video encoder 200 and video decoder 300 can encode or decode the current block 1200 using either an inter-frame prediction mode or an intra-frame prediction mode. Typically, once video encoder 200 and video decoder 300 determine the model used for encoding or decoding the current block 1200, they can perform operations according to the use of the determined model. For example, video encoder 200 and video decoder 300 can determine model parameters based on the determined model and apply those model parameters to samples to generate predicted samples (e.g., predicted blocks) for the current block 1200.

[0139] As mentioned in the examples above, there may be multiple models used for prediction. In video decoding, prediction schemes generally fall into two categories, where model selection is achieved through signal transmission and where models can be inherited from neighboring blocks. In inter-frame prediction, an example of the first approach is the AMVP (Advanced Motion Vector Prediction) mode, and an example of the second approach is the merge mode.

[0140] When using multiple models, an index can be signaled to the model candidate list to indicate the selected model. To reduce overhead, after reordering the list based on the cost metric for each model (e.g., sorting by ascending cost), only one model or fewer than the total number of models in the initial list can be signaled; in other words, only one or a few optimal models can be used.

[0141] The selected model index is then stored along with the block, or alternatively, the index of the total list can be stored. Since different blocks may have different costs for the same model, and different models may have the same index after the optional list is reordered and reduced in size as mentioned earlier, retaining the index of the total list can be beneficial because it uniquely identifies the model. In one example, this signaling could be applied to the AMVP pattern.

[0142] When a model inherits from adjacent blocks (spatially adjacent or non-adjacent) or temporally co-located blocks, such as in merge mode, the model's storage index is also inherited to indicate the model. Other types of adjacent blocks should be considered part of this description, as blocks from which the model inherits should not be considered as limiting the example technique.

[0143] If candidate list pruning is applied, candidates with the same or similar motions are generally removed from the list, and model information can be ignored during pruning or consideration. If considered, model costs can be compared during model pruning, and candidates with lower costs can be retained in the candidate list.

[0144] Several models can be combined to form a prediction. In some examples, several models can be selected from a list and weighted together. In one example, the model with the lowest cost can be selected for combination. The number of models selected from the list can be predefined or adaptive. In an adaptive example, a cost threshold can be used, and models with costs less than the threshold can be used.

[0145] The weighting factor can be fixed, such as 1 / N equal weights, where N is the number of models. Alternatively or additionally, higher-level signaling (in some examples, SPS, PPS, image, or slice headers) and weights can be sent via signaling at the block level.

[0146] In some examples, the weights can be derived from the video encoder 200 and the video decoder 300. In one example, the cost metric of the mode involved can be used to derive the weights, where larger weights are assigned to smaller cost models.

[0147] In a specific implementation example, the weight of the i-th pattern can be derived from the sum of the costs of all included models, as follows: w_i = (sum - cost_i) / (N-1) / sum, where w_i is the weight of the i-th model and is in the range of 1 to N, sum is the sum of the costs of N models, cost_i is the cost of the i-th model, and N is the number of patterns. In one example, the model cost can be the validation cost described above.

[0148] In some examples, the luminance and chrominance components can be used repeatedly with the same model, or the luminance and chrominance components can utilize different models. The method described above for LIC mode can be applied to cross-component intra-prediction schemes such as CCLM, CCCM, cross-component filtering (e.g., ALF), and other modes that use cross-component prediction, but the technique is not limited to any particular type of model and can be used with any model.

[0149] For cross-component prediction, the model is derived using adjacent reconstructed luminance and chrominance samples. In one example, the retained luminance and chrominance neighboring samples of the current block are validated by applying the model to luminance neighboring samples and calculating the difference between the samples after model application and chrominance neighboring samples. In one example, adjacent reconstructed samples may include spatially adjacent, spatially non-adjacent, and temporally co-located blocks, etc.

[0150] As an alternative to or supplement to the motion vectors described above, block vectors can be used. For block vectors, this would be an intra-block copy mode, such as IBC or IntraTMP in ECM. The difference from inter-frame prediction is that the current image is used as a reference, and the technique can be applied using the current image instead of the reference image.

[0151] Figure 2 This is a block diagram illustrating an example video encoder 200 that can perform the techniques of this disclosure. Figure 2 This disclosure is provided for illustrative purposes and should not be construed as a limitation on the techniques extensively illustrated and described herein. For illustrative purposes, this disclosure describes the video encoder 200 in accordance with the techniques of VVC and HEVC. However, the techniques of this disclosure can be performed by video encoding devices configured for other video decoding standards and video decoding formats, such as AV1 and subsequent formats of AV1 video decoding.

[0152] exist Figure 2In the example, the video encoder 200 includes a video data memory 230, a mode selection unit 202, a residual generation unit 204, a transform processing unit 206, a quantization unit 208, an inverse quantization unit 210, an inverse transform processing unit 212, a reconstruction unit 214, a filter unit 216, a decoded picture buffer (DPB) 218, and an entropy coding unit 220. Any or all of the video data memory 230, mode selection unit 202, residual generation unit 204, transform processing unit 206, quantization unit 208, inverse quantization unit 210, inverse transform processing unit 212, reconstruction unit 214, filter unit 216, DPB 218, and entropy coding unit 220 may be implemented in one or more processors or in processing circuitry. For example, the units of the video encoder 200 may be implemented as one or more circuit or logic elements as part of hardware circuitry, or as part of a processor, ASIC, or FPGA. Furthermore, the video encoder 200 may include additional or alternative processors or processing circuitry to perform these and other functions.

[0153] Video data storage 230 is an example of a storage system capable of storing video data to be encoded by components of video encoder 200. Video encoder 200 can receive data from, for example, video source 104 (…). Figure 1 The video encoder 200 receives video data stored in video data memory 230. DPB 218 is an example of a memory system that can act as a reference picture memory, storing reference video data for use by the video encoder 200 in predicting subsequent video data. Video data memory 230 and DPB 218 can each be formed from any of one or more memory devices or memory cells, such as dynamic random access memory (DRAM) (including synchronous DRAM (SDRAM)), magnetoresistive RAM (MRAM), resistive RAM (RRAM), or other types of memory devices. Video data memory 230 and DPB 218 can be provided by the same memory device or separate memory devices. In various examples, video data memory 230 can be on-chip (as illustrated) with other components of the video encoder 200, or off-chip relative to those components.

[0154] In this disclosure, references to video data memory 230 should not be construed as limited to memory inside video encoder 200 (unless specifically described) or memory outside video encoder 200 (unless specifically described). Rather, references to video data memory 230 should be understood as a reference memory that stores video data received by video encoder 200 for encoding (e.g., video data for the current block to be encoded). Figure 1 The memory 106 can also provide temporary storage for the outputs from various units of the video encoder 200.

[0155] Examples Figure 2 Various units help understand the operations performed by the video encoder 200. Units can be implemented as fixed-function circuits, programmable circuits, or combinations thereof. Fixed-function circuits are circuits that provide specific functionality and are pre-configured for the operations that can be performed. Programmable circuits are circuits that can be programmed to perform various tasks and provide flexible functionality for the operations that can be performed. For example, a programmable circuit can execute software or firmware that causes the programmable circuit to operate in a manner defined by the instructions of the software or firmware. Fixed-function circuits can execute software instructions (e.g., to receive or output parameters), but the type of operation performed by a fixed-function circuit is generally immutable. In some examples, one or more units in the unit may be different circuit blocks (fixed-function or programmable), and in some examples, one or more units in the unit may be integrated circuits.

[0156] The video encoder 200 may include an arithmetic logic unit (ALU), an essential function unit (EFU), digital circuitry, analog circuitry, and / or a programmable core, all formed by programmable circuitry. In an example where the operation of the video encoder 200 is performed using software executed by programmable circuitry, memory 106 ( Figure 1 The video encoder 200 may store instructions (e.g., target code) of the software received and executed by the video encoder 200, or another memory (not shown) within the video encoder 200 may store such instructions.

[0157] The video data storage unit 230 is configured to store received video data. The video encoder 200 can retrieve images of the video data from the video data storage unit 230 and provide the video data to the residual generation unit 204 and the mode selection unit 202. The video data in the video data storage unit 230 can be raw video data to be encoded.

[0158] The mode selection unit 202 includes a motion estimation unit 222, a motion compensation unit 224, and an intra-frame prediction unit 226. The mode selection unit 202 may include additional functional units for performing video prediction based on other prediction modes. As an example, the mode selection unit 202 may include a palette unit, an intra-frame block copying unit (which may be part of the motion estimation unit 222 and / or the motion compensation unit 224), an affine unit, a linear model (LM) unit, etc.

[0159] Mode selection unit 202 typically coordinates multiple coding channels to test combinations of coding parameters and the resulting rate-distortion values ​​for such combinations. Coding parameters may include the CTU-CU partitioning, the prediction mode for the CU, the transformation type of the residual data for the CU, the quantization parameters of the residual data for the CU, etc. Mode selection unit 202 can ultimately select a combination of coding parameters that has a better rate-distortion value compared to other tested combinations.

[0160] The video encoder 200 can divide images retrieved from the video data storage 230 into a series of CTUs, and encapsulate one or more CTUs within slices. The mode selection unit 202 can divide the image's CTUs according to the tree structure described above (such as an MTT structure, a QTBT structure, a superblock structure, or the quadtree structure described above). As described above, the video encoder 200 can form one or more CUs by dividing CTUs according to a tree structure. Such CUs are also commonly referred to as "video blocks" or "blocks".

[0161] Typically, mode selection unit 202 also controls its components (e.g., motion estimation unit 222, motion compensation unit 224, and intra-prediction unit 226) to generate prediction blocks for the current block (e.g., the current CU, or, in HEVC, the overlapping portion of PU and TU). To perform inter-frame prediction for the current block, motion estimation unit 222 may perform a motion search to identify one or more closely matching reference blocks in one or more reference pictures (e.g., one or more previously decoded pictures stored in DPB 218). Specifically, motion estimation unit 222 may calculate values ​​representing the similarity between a potential reference block and the current block, for example, based on sum of absolute differences (SAD), sum of squared differences (SSD), mean absolute difference (MAD), mean squared difference (MSD), etc. Motion estimation unit 222 may typically perform these calculations using sample-by-sample differences between the current block and the reference blocks under consideration. Motion estimation unit 222 may identify reference blocks with the lowest values ​​produced by these calculations to indicate the reference block that best matches the current block.

[0162] Motion estimation unit 222 can generate one or more motion vectors (MVs) that define the location of a reference block in a reference image relative to the location of the current block in the current image. Motion estimation unit 222 can then provide the motion vectors to motion compensation unit 224. For example, for unidirectional inter-frame prediction, motion estimation unit 222 can provide a single motion vector, while for bidirectional inter-frame prediction, motion estimation unit 222 can provide two motion vectors. Motion compensation unit 224 can then use the motion vectors to generate a prediction block. For example, motion compensation unit 224 can use the motion vectors to retrieve data for the reference block. As another example, where the motion vectors have fractional sample precision, motion compensation unit 224 can interpolate the values ​​of the prediction block according to one or more interpolation filters. Furthermore, for bidirectional inter-frame prediction, motion compensation unit 224 can retrieve data for two reference blocks identified by corresponding motion vectors and combine the retrieved data, for example, by per-sample averaging or weighted averaging.

[0163] When operating according to the AV1 video decoding format, the motion estimation unit 222 and the motion compensation unit 224 can be configured to encode the decoded blocks of video data (e.g., both luma and chroma decoded blocks) using translational motion compensation, affine motion compensation, overlap block motion compensation (OBMC), and / or composite inter-intra-frame prediction.

[0164] As another example, for intra-prediction or intra-prediction decoding, intra-prediction unit 226 may generate a prediction block from samples adjacent to the current block. For example, for directional mode, intra-prediction unit 226 may typically mathematically combine the values ​​of adjacent samples and fill these calculated values ​​across the current block in a defined direction to produce a prediction block. As another example, for DC mode, intra-prediction unit 226 may calculate the average of the adjacent samples of the current block and generate a prediction block to include the resulting average for each sample of the prediction block.

[0165] When operating according to the AV1 video decoding format, the intra-frame prediction unit 226 can be configured to encode decoded blocks of video data (e.g., both luma and chroma decoded blocks) using directional intra-frame prediction, non-directional intra-frame prediction, recursive filter intra-frame prediction, luma-chroma (CFL) prediction, intra-block copying (IBC), and / or palette modes. The mode selection unit 202 may include additional functional units for performing video prediction based on other prediction modes.

[0166] Mode selection unit 202 provides a prediction block to residual generation unit 204. Residual generation unit 204 receives an uncoded raw version of the current block from video data memory 230 and a prediction block from mode selection unit 202. Residual generation unit 204 calculates the sample-by-sample difference between the current block and the prediction block. The resulting sample-by-sample difference defines the residual block for the current block. In some examples, residual generation unit 204 may also determine the differences between sample values ​​in the residual block to generate the residual block using residual differential pulse decoding modulation (RDPCM). In some examples, residual generation unit 204 may be formed using one or more subtractor circuits performing binary subtraction.

[0167] In the example where mode selection unit 202 divides a CU into PUs, each PU can be associated with a luma prediction unit and a corresponding chroma prediction unit. Video encoder 200 and video decoder 300 can support PUs of various sizes. As noted above, the size of a CU can refer to the size of the luma decoding block of the CU, while the size of a PU can refer to the size of the luma prediction unit of the PU. Assuming a particular CU size is 2Nx2N, video encoder 200 can support PU sizes of 2Nx2N or NxN for intra-frame prediction, and symmetric PU sizes of 2Nx2N, 2NxN, Nx2N, NxN, or similar for inter-frame prediction. Video encoder 200 and video decoder 300 can also support asymmetric partitioning for PU sizes of 2NxnU, 2NxnD, nLx2N, and nRx2N for inter-frame prediction.

[0168] In an example where mode selection unit 202 does not further divide the CU into PUs, each CU can be associated with a luminance decoding block and a corresponding chrominance decoding block. As mentioned above, the size of the CU can refer to the size of the luminance decoding block of the CU. The video encoder 200 and the video decoder 300 can support CU sizes of 2Nx2N, 2NxN, or Nx2N.

[0169] For other video decoding techniques, such as intra-block copy mode decoding, affine mode decoding, and linear model (LM) mode decoding, as some examples, mode selection unit 202 generates a prediction block for the current block being encoded via a corresponding unit associated with the decoding technique. In some examples (such as palette mode decoding), mode selection unit 202 may not generate a prediction block, but instead generate syntax elements indicating how the block is reconstructed based on a selected palette. In such modes, mode selection unit 202 may provide these syntax elements to entropy coding unit 220 for encoding.

[0170] As described above, the residual generation unit 204 receives video data for the current block and the corresponding prediction block. Then, the residual generation unit 204 generates a residual block for the current block. To generate the residual block, the residual generation unit 204 calculates the sample-by-sample difference between the prediction block and the current block.

[0171] Transform processing unit 206 applies one or more transformations to the residual block to generate a block of transform coefficients (referred to herein as a "transform coefficient block"). Transform processing unit 206 may apply various transformations to the residual block to form the transform coefficient block. For example, transform processing unit 206 may apply a discrete cosine transform (DCT), direction transform, Karhunen-Loeve transform (KLT), or conceptually similar transformations to the residual block. In some examples, transform processing unit 206 may perform multiple transformations on the residual block, such as primary and secondary transformations (e.g., rotation transformations). In some examples, transform processing unit 206 does not apply any transformations to the residual block.

[0172] When operating according to AV1, transform processing unit 206 may apply one or more transforms to the residual block to generate a block of transform coefficients (referred to herein as a "transform coefficient block"). Transform processing unit 206 may apply various transforms to the residual block to form the transform coefficient block. For example, transform processing unit 206 may apply a combination of horizontal / vertical transforms, which may include the Discrete Cosine Transform (DCT), the Asymmetric Discrete Sine Transform (ADST), the Reversed ADST (e.g., ADST in reverse order), and the Identity Transform (IDTX). When using the Identity Transform, the transform is skipped in either the vertical or horizontal direction. In some examples, transform processing may be skipped entirely.

[0173] Quantization unit 208 quantizes the transform coefficients in a transform coefficient block to produce a quantized transform coefficient block. Quantization unit 208 quantizes the transform coefficients of the transform coefficient block according to the quantization parameter (QP) value associated with the current block. Video encoder 200 (e.g., via mode selection unit 202) can adjust the degree of quantization applied to the transform coefficient block associated with the current block by adjusting the QP value associated with the CU. Quantization may cause information loss, and therefore, the quantized transform coefficients may have lower accuracy compared to the original transform coefficients produced by transform processing unit 206.

[0174] The inverse quantization unit 210 and the inverse transform processing unit 212 can apply inverse quantization and inverse transform, respectively, to the quantized transform coefficient block to reconstruct the residual block based on the transform coefficient block. The reconstruction unit 214 can generate a reconstructed block corresponding to the current block (although potentially with some degree of distortion) based on the reconstructed residual block and the prediction block generated by the mode selection unit 202. For example, the reconstruction unit 214 can add samples of the reconstructed residual block to corresponding samples of the prediction block generated by the mode selection unit 202 to generate the reconstructed block.

[0175] Filter unit 216 may perform one or more filtering operations on the reconstructed block. For example, filter unit 216 may perform a deblocking operation to reduce block artifacts along the edges of the CU. In some examples, the operation of filter unit 216 may be skipped.

[0176] When operating according to AV1, filter unit 216 may perform one or more filtering operations on the reconstructed block. For example, filter unit 216 may perform a deblocking operation to reduce block artifacts along the edges of the CU. In other examples, filter unit 216 may apply a constrained direction enhancement filter (CDEF) after deblocking and may include the application of a non-separable, nonlinear, low-pass directional filter based on the estimated edge direction. Filter unit 216 may also include a loop recovery filter applied after CDEF and may include a separable symmetric normalized Wiener filter or a dual-guided filter.

[0177] The video encoder 200 stores reconstructed blocks in the DPB 218. For example, in an example where the filter unit 216 is not operated, the reconstruction unit 214 may store reconstructed blocks in the DPB 218. In an example where the filter unit 216 is operated, the filter unit 216 may store filtered reconstructed blocks in the DPB 218. The motion estimation unit 222 and the motion compensation unit 224 may retrieve reference images formed by the reconstructed (and potentially filtered) blocks from the DPB 218 to perform inter-frame prediction for blocks of subsequent encoded images. Additionally, the intra-frame prediction unit 226 may use the reconstructed blocks of the current image in the DPB 218 to perform intra-frame prediction for other blocks in the current image.

[0178] Typically, entropy coding unit 220 can entropy-encode syntax elements received from other functional components of video encoder 200. For example, entropy coding unit 220 can entropy-encode quantized transform coefficient blocks from quantization unit 208. As another example, entropy coding unit 220 can entropy-encode predictive syntax elements (e.g., motion information for inter-frame prediction or intra-frame mode information for intra-frame prediction) from mode selection unit 202. Entropy coding unit 220 can perform one or more entropy coding operations on syntax elements (another example of video data) to generate entropy-coded data. For example, entropy coding unit 220 can perform context-adaptive variable-length decoding (CAVLC), CABAC, variable-to-variable (V2V) length decoding, syntax-based context-adaptive binary arithmetic decoding (SBAC), probability interval partitioning entropy (PIPE) decoding, exponential Golomb coding, or another type of entropy coding operation on the data. In some examples, entropy coding unit 220 can operate in a bypass mode where syntax elements are not entropy-encoded.

[0179] The video encoder 200 can output a bitstream that includes the entropy coding syntax elements required to reconstruct slices or blocks of images. Specifically, the entropy coding unit 220 can output a bitstream.

[0180] According to AV1, entropy coding unit 220 can be configured as a symbol-to-symbol adaptive multi-symbol arithmetic decoder. The syntax elements in AV1 consist of an N-element alphabet, and the context (e.g., a probability model) consists of a set of N probabilities. Entropy coding unit 220 can store the probabilities as an n-bit (e.g., 15-bit) cumulative distribution function (CDF). Entropy coding unit 220 can perform recursive scaling to update the context using an update factor based on the alphabet size.

[0181] The operations described above are relative to blocks. This description should be understood as operations applied to luma decoding blocks and / or chroma decoding blocks. As described above, in some examples, the luma decoding block and chroma decoding block are the luma and chroma components of the CU. In some examples, the luma decoding block and chroma decoding block are the luma and chroma components of the PU.

[0182] In some examples, it is not necessary to repeat the operations performed relative to the luma decoder for the chroma decoder block. As an example, the operations for identifying the motion vector (MV) and reference image of the luma decoder block do not need to repeat the MV and reference image used to identify the chroma block. Instead, the MV used for the luma decoder block can be scaled to determine the MV used for the chroma block, and the reference image can be the same. As another example, the intra-frame prediction process can be the same for both the luma and chroma decoders.

[0183] Video encoder 200 represents an example of a device configured to encode video data, the device including a memory configured to store the video data and one or more processing units implemented in circuitry and configured to, for each of a plurality of models, determine a corresponding cost value associated with a corresponding model among the plurality of models to generate a corresponding cost value for the plurality of models, determine, based on the corresponding cost value, a model among the plurality of models for encoding the current block of video data, and encode the current block based on the determined model.

[0184] Figure 3 This is a block diagram illustrating an example video decoder 300 that can perform the techniques of this disclosure. Figure 3 This disclosure is provided for illustrative purposes and not for limiting the techniques extensively illustrated and described herein. For illustrative purposes, the video decoder 300 is described in accordance with VVC and HEVC techniques. However, the techniques of this disclosure can be implemented by video decoding devices configured for other video decoding standards.

[0185] exist Figure 3 In the example, the video decoder 300 includes a decoded picture buffer (CPB) memory 320, an entropy decoding unit 302, a prediction processing unit 304, an inverse quantization unit 306, an inverse transform processing unit 308, a reconstruction unit 310, a filter unit 312, and a DPB 314. Any or all of the CPB memory 320, entropy decoding unit 302, prediction processing unit 304, inverse quantization unit 306, inverse transform processing unit 308, reconstruction unit 310, filter unit 312, and DPB 314 can be implemented in one or more processors or in processing circuitry. For example, the units of the video decoder 300 can be implemented as one or more circuit or logic elements as part of hardware circuitry, or as part of a processor, ASIC, or FPGA. Furthermore, the video decoder 300 may include additional or alternative processors or processing circuitry to perform these and other functions.

[0186] The prediction processing unit 304 includes a motion compensation unit 316 and an intra-prediction unit 318. The prediction processing unit 304 may include additional units that perform predictions based on other prediction modes. As an example, the prediction processing unit 304 may include a palette unit, an intra-block copying unit (which may form part of the motion compensation unit 316), an affine unit, a linear model (LM) unit, etc. In other examples, the video decoder 300 may include more, fewer, or different functional components.

[0187] When operating according to AV1, motion compensation unit 316 can be configured to decode video data blocks (e.g., both luma and chroma blocks) using translational motion compensation, affine motion compensation, OBMC, and / or composite inter-intra-frame prediction, as described above. Intra-frame prediction unit 318 can be configured to decode video data blocks (e.g., both luma and chroma blocks) using directional intra-frame prediction, non-directional intra-frame prediction, recursive filter intra-frame prediction, CFL, IBC, and / or palette mode, as described above.

[0188] CPB memory 320 is an example of a memory system capable of storing video data (such as encoded video bitstreams) to be decoded by components of video decoder 300. For example, it can be stored from computer-readable medium 110 ( Figure 1 The video data stored in the CPB memory 320 is obtained. The CPB memory 320 may include a CPB that stores encoded video data (e.g., syntax elements) from the encoded video bitstream. Furthermore, the CPB memory 320 may store video data other than the syntax elements of the decoded picture, such as temporary data representing the output from various units of the video decoder 300. The DPB 314 is an example of a memory system that typically stores a decoded picture, which the video decoder 300 may output, and / or uses as reference video data when decoding subsequent data or pictures of the encoded video bitstream. The CPB memory 320 and the DPB 314 may each be formed from any of various memory devices or memory cells, such as DRAM (including SDRAM), MRAM, RRAM, or other types of memory devices. The CPB memory 320 and the DPB 314 may be provided by the same memory device or separate memory devices. In various examples, the CPB memory 320 may be on-chip with other components of the video decoder 300, or off-chip relative to those components.

[0189] Additionally or alternatively, in some examples, the video decoder 300 may be from the memory 120 ( Figure 1 The decoded video data can be retrieved from the memory. In other words, memory 120 can utilize CPB memory 320 to store data as discussed above. Similarly, when some or all of the functionality of video decoder 300 is implemented in software to be executed by the processing circuitry of video decoder 300, memory 120 can store instructions to be executed by video decoder 300.

[0190] Examples Figure 3 The various units shown help to understand the operations performed by the video decoder 300. These units can be implemented as fixed-function circuits, programmable circuits, or a combination thereof. Similar to... Figure 2Fixed-function circuits are circuits that provide specific functionality and are pre-configured for the operations they can perform. Programmable circuits are circuits that can be programmed to perform various tasks and provide flexible functionality for the operations they can perform. For example, a programmable circuit can execute software or firmware that causes the programmable circuit to operate in a manner defined by the instructions of the software or firmware. Fixed-function circuits can execute software instructions (e.g., to receive or output parameters), but the type of operation performed by a fixed-function circuit is typically immutable. In some examples, one or more units in a cell may be different circuit blocks (fixed-function or programmable), and in some examples, one or more units in a cell may be integrated circuits.

[0191] The video decoder 300 may include an ALU, EFU, digital circuitry, analog circuitry, and / or a programmable core formed by programmable circuitry. In an example where the operation of the video decoder 300 is performed by software executed on programmable circuitry, on-chip or off-chip memory may store instructions (e.g., object code) of the software received and executed by the video decoder 300.

[0192] The entropy decoding unit 302 can receive encoded video data from the CPB and perform entropy decoding on the video data to reproduce the syntax elements. The prediction processing unit 304, the inverse quantization unit 306, the inverse transform processing unit 308, the reconstruction unit 310, and the filter unit 312 can generate decoded video data based on the syntax elements extracted from the bitstream.

[0193] Typically, the video decoder 300 reconstructs the image block by block. The video decoder 300 can perform the reconstruction operation on each block individually (where the block currently being reconstructed (i.e., decoded) can be referred to as the "current block").

[0194] Entropy decoding unit 302 can entropy decode the syntax elements of the quantized transform coefficients defining the quantized transform coefficient block, as well as transform information (such as quantization parameters (QP) and / or transform mode indications). Inverse quantization unit 306 can use the QP associated with the quantized transform coefficient block to determine the degree of quantization, and similarly, determine the degree of inverse quantization to be applied by inverse quantization unit 306. Inverse quantization unit 306 can, for example, perform a bit-by-bit left shift operation to inverse quantize the quantized transform coefficients. Inverse quantization unit 306 can thereby form a transform coefficient block including the transform coefficients.

[0195] After the inverse quantization unit 306 forms the transform coefficient block, the inverse transform processing unit 308 may apply one or more inverse transforms to the transform coefficient block to generate a residual block associated with the current block. For example, the inverse transform processing unit 308 may apply an inverse DCT, an inverse integer transform, an inverse Karhunen-Loeve transform (KLT), an inverse rotation transform, an inverse direction transform, or another inverse transform to the transform coefficient block.

[0196] Furthermore, the prediction processing unit 304 generates a prediction block based on the prediction information syntax elements entropy decoded by the entropy decoding unit 302. For example, if the prediction information syntax elements indicate that the current block is an inter-frame prediction, the motion compensation unit 316 can generate the prediction block. In this case, the prediction information syntax elements may indicate the reference picture from which the reference block is to be retrieved in the DPB 314, and a motion vector identifying the position of the reference block in the reference picture relative to the position of the current block in the current picture. The motion compensation unit 316 may generally follow the same procedure as relative to the motion compensation unit 224 ( Figure 2 The method described is essentially the same as the method used to perform the inter-frame prediction process.

[0197] As another example, when the prediction information syntax element indicates that the current block is intra-predictive, intra-predictive unit 318 may generate a prediction block according to the intra-predictive mode indicated by the prediction information syntax element. Similarly, intra-predictive unit 318 may generally follow the same procedure as relative to intra-predictive unit 226 ( Figure 2 The intra-prediction process is performed in a manner substantially similar to that described above. The intra-prediction unit 318 can retrieve data of neighboring samples of the current block from the DPB 314.

[0198] Reconstruction unit 310 can use the prediction block and the residual block to reconstruct the current block. For example, reconstruction unit 310 can add samples from the residual block to the corresponding samples from the prediction block to reconstruct the current block.

[0199] Filter unit 312 can perform one or more filtering operations on the reconstructed block. For example, filter unit 312 can perform a deblocking operation to reduce block artifacts along the edges of the reconstructed block. The operation of filter unit 312 is not necessarily performed in all examples.

[0200] The video decoder 300 can store reconstructed blocks in the DPB 314. For example, in an example where the operation of the filter unit 312 is not performed, the reconstruction unit 310 can store the reconstructed blocks in the DPB 314. In an example where the operation of the filter unit 312 is performed, the filter unit 312 can store the filtered reconstructed blocks in the DPB 314. As discussed above, the DPB 314 can provide reference information (such as samples of the current image for intra-frame prediction and samples of previously decoded images for subsequent motion compensation) to the prediction processing unit 304. Furthermore, the video decoder 300 can output decoded images (e.g., decoded video) from the DPB 314 for use in applications such as... Figure 1 The subsequent presentation on display devices such as display device 118.

[0201] In this manner, video decoder 300 represents an example of a video decoding device, which includes a memory configured to store video data and one or more processing units implemented in a circuit and configured to, for each of a plurality of models, determine a corresponding cost value associated with a corresponding model among the plurality of models to generate a corresponding cost value for the plurality of models, determine, based on the corresponding cost value, a model among the plurality of models for encoding the current block of video data, and encode the current block based on the determined model.

[0202] Figure 4 This is a flowchart illustrating an example method for encoding the current block according to the technology of this disclosure. The current block may be or may include the current CU. Although this relates to video encoder 200 ( Figure 1 and Figure 2 This is a description, but it should be understood that other devices can be configured to perform the same actions. Figure 4 Similar to the method.

[0203] In this example, the video encoder 200 initially predicts the current block (400). For example, the video encoder 200 may form a prediction block for the current block. The video encoder 200 may then compute a residual block for the current block (402). To compute the residual block, the video encoder 200 may compute the difference between the unencoded original block for the current block and the prediction block. The video encoder 200 may then transform the residual block and quantize the transform coefficients of the residual block (404). Next, the video encoder 200 may scan the quantized transform coefficients of the residual block (406). During or after the scan, the video encoder 200 may entropy encode the transform coefficients (408). For example, the video encoder 200 may use CAVLC or CABAC to encode the transform coefficients. The video encoder 200 may then output the entropy-encoded data of the block (410).

[0204] Figure 5 This is a flowchart illustrating an example method for decoding a current block of video data according to the technology of this disclosure. The current block may be or may include the current CU. Although this relates to video decoder 300 ( Figure 1 and Figure 3 This is a description, but it should be understood that other devices can be configured to perform the same actions. Figure 5 Similar to the method.

[0205] The video decoder 300 may receive entropy-coded data for the current block, such as entropy-coded prediction information and entropy-coded data for the transform coefficients of the residual block corresponding to the current block (500). The video decoder 300 may entropy decode the entropy-coded data to determine prediction information for the current block and reproduce the transform coefficients of the residual block (502). The video decoder 300 may, for example, use an intra-frame prediction mode or inter-frame prediction mode indicated by the prediction information of the current block to predict the current block (504) to compute a prediction block for the current block. The video decoder 300 may then perform an inverse scan on the reproduced transform coefficients (506) to create a block of quantized transform coefficients. The video decoder 300 may then inverse quantize the transform coefficients and apply an inverse transform to the transform coefficients to produce a residual block (508). The video decoder 300 may finally decode the current block by combining the prediction block and the residual block (510).

[0206] Figure 13 This is a flowchart illustrating one or more examples as described in this disclosure. Figure 13 The example techniques can be performed by the processing circuitry of the video encoder 200 or video decoder 300. For example, one or more memories configured to store the video data. Examples of one or more memories include memory 106, memory 120, video data memory 230, decoded image buffer 218, CPB memory 320, DPB 314, or some other memory of the video encoder 200 or video decoder 300. The processing circuitry (e.g., of the video encoder 200 or video decoder 300) can be coupled to one or more memories and configured to perform the example techniques described in this disclosure, such as... Figure 13 Those technologies. For convenience, we also referenced... Figure 12 .

[0207] For each of the multiple models, the processing circuitry can use a first set of samples to derive the corresponding model parameters (1300) associated with the corresponding model among the multiple models. The multiple models include Local Intensity Compensation (LIC), Linear Model (LM), Convolutional Cross Component Model (CCCM), Gradient-and-Position-Based CCCM (GL-CCCM), Cross Component Residual Model (CCRM), Block Vector Guided CCCM (BVG-CCCM), and Cross Component Merge (CCMerge). In some examples, at least one of the multiple models is a model used to encode or decode temporally or spatially adjacent blocks of the current block.

[0208] For CCLM, the model parameters can be a nonlinear term P and a bias term B. The model parameters can be a nonlinear term derived from samples L0 to L3 and an offset used for CCLM using unsampled luminance samples (non-luminance subsampling). Vertical and horizontal gradients, along with the nonlinear term P and the bias term B, can be examples of model parameters for GL-CCCM. These are some examples of model parameters, and similar other model parameters may exist for different models. As described above, the processing circuit can use model parameter derivation techniques for the corresponding model to determine the model parameters, such as the examples above.

[0209] The first set of samples may include samples that are not immediately adjacent to the current block. For example, the first set of samples may be sample 1208, because sample 1208 is not immediately adjacent to the current block 1200, but is in the same image as the current block 1200. The first set of samples may include samples that are not immediately adjacent to the current block and samples that are not immediately adjacent to the reference block. For example, the first set of samples may be sample 1208 and / or sample 1210, because sample 1208 is not immediately adjacent to the current block 1200, but is in the same image as the current block 1200, and sample 1210 is not immediately adjacent to the reference block 1202, but is in the same image as the reference block 1202. The reference block 1202 is identified by the motion vector used for the current block 1200.

[0210] The processing circuitry can use the corresponding model parameters and the second set of samples to determine the corresponding cost values ​​associated with the corresponding model among a plurality of models, thereby generating the corresponding cost values ​​for the plurality of models (1302). The first set of samples and the second set of samples are mutually exclusive. In this way, the processing circuitry can determine the model parameters for the model on one set of samples (e.g., the first set of samples), but determine the cost values ​​associated with the model on a different set of samples (e.g., the second set of samples).

[0211] For example, the second set of samples may include samples immediately adjacent to the current block, such as sample 1204 in the same image as the current block 1200, wherein the first set of samples includes samples not immediately adjacent to the current block 1200 (e.g., sample 1208). As another example, the second set of samples may include samples immediately adjacent to the current block, such as sample 1204 in the same image as the current block 1200, and samples immediately adjacent to the reference block 1202, such as sample 1210 in the same image as the reference block 1202, wherein the first set of samples includes samples not immediately adjacent to the current block 1200 (e.g., sample 1208) and samples not immediately adjacent to the reference block 1202 (e.g., sample 1210).

[0212] To determine the corresponding cost value, the processing circuitry can generate predicted samples for the second set of samples based on the model parameters for each of the multiple models. That is, even though the second set of samples is available, the processing circuitry can continue as if the second set of samples were being encoded or decoded. The processing circuitry can determine the difference between the predicted samples and the second set of samples, and determine the cost value associated with the model based on the determined difference.

[0213] As an example, the processing circuit may determine a first prediction signal associated with a first model based on model parameters of a first model among multiple models, and determine a first cost value among the corresponding cost values ​​based on the difference between the first prediction signal and a second set of samples. The processing circuit may also determine a second prediction signal associated with a second model based on model parameters of a second model among multiple models, and determine a second cost value among the corresponding cost values ​​based on the difference between the second prediction signal and a second set of samples.

[0214] The processing circuitry can determine, based on a corresponding cost value, which model from a plurality of models will be used to encode or decode the current block (1304). As an example, the processing circuitry can select the model associated with the lowest cost value among the corresponding cost values. As another example, the processing circuitry can construct a list of candidate models based on the corresponding cost values ​​and select a model from the list of candidate models. For example, the processing circuitry can construct a list of candidate models in which the candidates are sorted based on the corresponding cost values.

[0215] The processing circuitry can encode or decode the current block based on the determined model (1306). For example, the processing circuitry can encode or decode the current block using an inter-frame prediction mode or an intra-frame prediction mode.

[0216] The following numbered clauses illustrate one or more aspects of the devices and technologies described in this disclosure.

[0217] Clause 1A. A method for decoding video data, the method comprising: for each of a plurality of models, determining a corresponding cost value associated with a corresponding model among the plurality of models to generate a corresponding cost value for the plurality of models; determining, based on the corresponding cost value, a model among the plurality of models for decoding a current block of video data; and decoding the current block based on the determined model.

[0218] Clause 2A. The method according to Clause 1A, wherein determining the model includes selecting the model associated with the lowest cost value among the respective cost values.

[0219] Clause 3A. The method according to any one of Clauses 1A and 2A, the method further comprising: constructing a list of candidate models based on the corresponding cost value.

[0220] Clause 4A. The method described in Clause 3A, wherein determining the model includes receiving an index of the list of models.

[0221] Clause 5A. The method according to any one of Clauses 3A and 4A, the method further comprising: constructing an initial model list, wherein constructing the model list includes at least one of: pruning the initial model list to construct the model list; or determining a valid range of indices in the initial model list, wherein the model list includes the indices within the valid range of the initial model list.

[0222] Clause 6A. The method according to any one of Clauses 1A to 5A, wherein determining the corresponding cost comprises: for each of the plurality of models, applying the model to samples used to derive the model; determining the difference between the samples after model application and the target samples; and determining a cost value associated with the model based on the determined difference.

[0223] Clause 7A. The method according to any one of Clauses 1A to 5A, wherein determining the corresponding cost comprises: for each of the plurality of models, applying the model to samples not used to derive the model; determining the difference between the samples after model application and the target samples; and determining a cost value associated with the model based on the determined difference.

[0224] Clause 8A. The method according to any one of Clauses 6A and 7A, wherein the target sample includes samples adjacent to the current block.

[0225] Clause 9A. The method according to any one of Clauses 1A to 8A, wherein determining the corresponding cost value includes determining the corresponding cost value based on samples used for model validation.

[0226] Clause 10A. The method according to any one of Clauses 1A to 9A, the method further comprising: associating at least one model in the models with an index of the current block or a list of candidate models identifying the model; storing information indicating the association; and decoding subsequent blocks based on the information indicating the association.

[0227] Clause 11A. The method according to any one of Clauses 1A to 10A, wherein the model is a first model, the method further comprising: determining a second model for decoding the current block, wherein decoding the current block includes decoding the current block based on the first model and the second model.

[0228] Clause 12A. The method according to any one of Clauses 1A to 11A, wherein decoding includes decoding.

[0229] Clause 13A. The method according to any one of Clauses 1A to 12A, wherein decoding includes encoding.

[0230] Clause 14A. An apparatus for decoding video data, the apparatus comprising: one or more memories configured to store the video data; and one or more processors implemented in a circuit, coupled to the one or more memories, and configured to perform a method according to any one of Clauses 1A to 13A or a combination thereof.

[0231] Clause 15A. The device as described in Clause 14A further includes a display configured to display decoded video data.

[0232] Clause 16A. The device pursuant to any one of Clauses 14A and 15A, wherein the device comprises one or more of a camera, computer, mobile device, broadcast receiver device or set-top box.

[0233] Clause 17A. The device pursuant to any one of Clauses 14A to 16A, wherein the device includes a video decoder.

[0234] Clause 18A. The device pursuant to any one of Clauses 14A to 17A, wherein the device includes a video encoder.

[0235] Clause 19A. An apparatus for decoding video data, the apparatus comprising one or more components for performing the method according to any one of Clauses 1A to 13A.

[0236] Clause 20A. A computer-readable storage medium having instructions stored thereon, which, when executed, cause one or more processors to perform the method according to any one of Clauses 1A to 13A.

[0237] Clause 1. A method for encoding or decoding video data, the method comprising: for each of a plurality of models, using a first set of samples to derive a corresponding model parameter associated with a corresponding model in the plurality of models; using the corresponding model parameter and a second set of samples to determine a corresponding cost value associated with the corresponding model in the plurality of models to generate a corresponding cost value for the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; determining, based on the corresponding cost value, a model in the plurality of models for encoding or decoding a current block; and encoding or decoding the current block based on the determined model.

[0238] Clause 2. The method according to Clause 1, wherein encoding or decoding the current block includes encoding or decoding the current block in an inter-frame prediction mode or an intra-frame prediction mode.

[0239] Clause 3. The method according to any one of Clauses 1 and 2, wherein the second set of samples includes samples immediately adjacent to the current block, and the first set of samples includes samples not immediately adjacent to the current block.

[0240] Clause 4. The method according to any one of Clauses 1 to 3, wherein the second set of samples includes samples immediately adjacent to the current block and samples immediately adjacent to the reference block, and the first set of samples includes samples not immediately adjacent to the current block and samples not immediately adjacent to the reference block.

[0241] Clause 5. The method according to any one of Clauses 1 to 4, wherein determining the corresponding cost value comprises: for each of the plurality of models, generating a predicted sample for the second set of samples based on the model parameters; determining the difference between the predicted sample and the second set of samples; and determining a cost value associated with the model based on the determined difference.

[0242] Clause 6. The method according to any one of Clauses 1 to 5, wherein determining the corresponding cost value comprises: determining a first prediction signal associated with the first model based on model parameters of a first model among the plurality of models; determining a first cost value among the corresponding cost values ​​based on the difference between the first prediction signal and the second set of samples; determining a second prediction signal associated with the second model based on model parameters of a second model among the plurality of models; and determining a second cost value among the corresponding cost values ​​based on the difference between the second prediction signal and the second set of samples.

[0243] Clause 7. The method according to any one of Clauses 1 to 6, wherein the plurality of models includes Local Intensity Compensation (LIC), Linear Model (LM), Convolutional Cross Component Model (CCCM), Gradient and Position-Based CCCM (GL-CCCM), Cross Component Residual Model (CCRM), Block Vector Guided CCCM (BVG-CCCM), and Cross Component Merge (CCMerge).

[0244] Clause 8. The method according to any one of Clauses 1 to 7, wherein determining the model includes selecting the model associated with the lowest cost value among the respective cost values.

[0245] Clause 9. The method according to any one of Clauses 1 to 8, the method further comprising: constructing a candidate model list, wherein the candidates are sorted in the model list based on the corresponding cost value, wherein determining the model includes selecting the model from the candidate model list.

[0246] Clause 10. The method according to any one of Clauses 1 to 9, wherein at least one of the plurality of models is a model for encoding or decoding temporally or spatially adjacent blocks of the current block.

[0247] Clause 11. An apparatus for encoding or decoding video data, the apparatus comprising: one or more memories configured to store the video data; and processing circuitry coupled to the one or more memories, wherein the processing circuitry is configured to: for each of a plurality of models, derive corresponding model parameters associated with a corresponding model among the plurality of models using a first set of samples; determine corresponding cost values ​​associated with the corresponding model among the plurality of models using the corresponding model parameters and a second set of samples to generate corresponding cost values ​​for the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; determine a model among the plurality of models for encoding or decoding a current block based on the corresponding cost values; and encode or decode the current block based on the determined model.

[0248] Clause 12. The apparatus according to Clause 11, wherein, in order to encode or decode the current block, the processing circuitry is configured to encode or decode the current block in an inter-frame prediction mode or an intra-frame prediction mode.

[0249] Clause 13. The device according to any one of Clauses 11 and 12, wherein the second set of samples includes samples immediately adjacent to the current block, and the first set of samples includes samples not immediately adjacent to the current block.

[0250] Clause 14. The device according to any one of Clauses 11 to 13, wherein the second set of samples includes samples immediately adjacent to the current block and samples immediately adjacent to the reference block, and the first set of samples includes samples not immediately adjacent to the current block and samples not immediately adjacent to the reference block.

[0251] Clause 15. The device according to any one of Clauses 11 to 14, wherein, in order to determine the respective cost value, the processing circuitry is configured to: for each of the plurality of models, generate a predicted sample for the second set of samples based on the model parameters; determine the difference between the predicted sample and the second set of samples; and determine a cost value associated with the model based on the determined difference.

[0252] Clause 16. The device according to any one of Clauses 11 to 15, wherein, in order to determine the respective cost value, the processing circuitry is configured to: determine a first prediction signal associated with the first model based on model parameters of a first model among the plurality of models; determine a first cost value among the respective cost values ​​based on the difference between the first prediction signal and a second set of samples; determine a second prediction signal associated with the second model based on model parameters of a second model among the plurality of models; and determine a second cost value among the respective cost values ​​based on the difference between the second prediction signal and the second set of samples.

[0253] Clause 17. The device according to any one of Clauses 11 to 16, wherein the plurality of models includes Local Intensity Compensation (LIC), Linear Model (LM), Convolutional Cross Component Model (CCCM), Gradient-and-Position-Based CCCM (GL-CCCM), Cross Component Residual Model (CCRM), Block Vector Guided CCCM (BVG-CCCM), and Cross Component Merge (CCMerge).

[0254] Clause 18. The device according to any one of Clauses 11 to 17, wherein, in order to determine the model, the processing circuitry is configured to select the model associated with the lowest cost value among the respective cost values.

[0255] Clause 19. The device according to any one of Clauses 11 to 18, wherein the processing circuitry is configured to: construct a candidate model list, wherein the candidates are sorted in the model list based on the corresponding cost value, wherein, in order to determine the model, the processing circuitry is configured to select the model from the candidate model list.

[0256] Clause 20. One or more computer-readable storage media having instructions thereon, which, when executed, cause one or more processors to: for each of a plurality of models, derive a corresponding model parameter associated with a corresponding corresponding model among the plurality of models using a first set of samples; determine a corresponding cost value associated with the corresponding model among the plurality of models using the corresponding model parameters and a second set of samples to generate a corresponding cost value for the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; determine a model among the plurality of models for encoding or decoding the current block based on the corresponding cost value; and encode or decode the current block based on the determined model.

[0257] It should be recognized that, based on the examples, certain actions or events of any technique described herein may be performed in a different order, and may be added, combined, or omitted entirely (e.g., not all actions or events described are necessary for implementing the technique). Furthermore, in some examples, actions or events may be performed concurrently (e.g., through multithreading, interrupt handling, or multiple processors) rather than sequentially.

[0258] In one or more examples, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality may be stored as one or more instructions or code on a computer-readable medium or transmitted via a computer-readable medium and executed by a hardware-based processing unit. A computer-readable medium may include a computer-readable storage medium (which corresponds to a tangible medium such as a data storage medium) or a communication medium, including, for example, any medium that facilitates the transfer of a computer program from one place to another according to a communication protocol. Thus, a computer-readable medium may generally correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium such as a signal or carrier wave. A data storage medium may be any available medium that can be accessed by one or more computers or one or more processors to retrieve instructions, code, and / or data structures for implementing the techniques described in this disclosure. Computer program products may include computer-readable media.

[0259] By way of example, and not limitation, such computer-readable storage media may include one or more of RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. Furthermore, any connection is appropriately referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology (such as infrared, radio, and microwave), then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technology (such as infrared, radio, and microwave) is included in the definition of medium. However, it should be understood that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but instead refer to non-transient tangible storage media. As used herein, disks and optical discs include compact optical discs (CDs), laser optical discs, optical discs, digital versatile optical discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs use lasers to reproduce data optically. Combinations of these should also be included within the scope of computer-readable media.

[0260] Instructions can be executed by one or more processors, such as one or more DSPs, general-purpose microprocessors, ASICs, FPGAs, or other equivalent integrated or discrete logic circuits. Therefore, the terms "processor" and "processing circuit" as used herein can refer to any of the foregoing structures or any other structure suitable for implementing the techniques described herein. Additionally, in some aspects, the functionality described herein can be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated into combined codecs. Furthermore, these techniques can be fully implemented in one or more circuit or logic elements.

[0261] The techniques disclosed herein can be implemented in a wide variety of devices or apparatuses, including wireless mobile phones, integrated circuits (ICs), or a set of ICs (e.g., chipsets). Various components, modules, or units are described in this disclosure to emphasize functional aspects of a device configured to perform the disclosed techniques, but implementation by different hardware units is not necessarily required. Specifically, as described above, various units may be combined in a codec hardware unit, or various units may be provided by a collection of interoperable hardware units (including one or more processors as described above) combined with appropriate software and / or firmware.

[0262] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

1. A method for encoding or decoding video data, the method comprising: For each of the multiple models, the corresponding model parameters associated with the corresponding model among the multiple models are derived using the first set of samples; The corresponding cost value associated with the corresponding model among the plurality of models is determined using the corresponding model parameters and the second set of samples, so as to generate the corresponding cost value of the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; The model used for encoding or decoding the current block is determined based on the corresponding cost value; as well as The current block is encoded or decoded based on the determined model.

2. The method of claim 1, wherein encoding or decoding the current block comprises encoding or decoding the current block in an inter-frame prediction mode or an intra-frame prediction mode.

3. The method of claim 1, wherein the second set of samples includes samples immediately adjacent to the current block, and the first set of samples includes samples not immediately adjacent to the current block.

4. The method of claim 1, wherein the second set of samples includes samples immediately adjacent to the current block and samples immediately adjacent to the reference block, and the first set of samples includes samples not immediately adjacent to the current block and samples not immediately adjacent to the reference block.

5. The method of claim 1, wherein determining the corresponding cost value comprises: For each of the plurality of models, a predicted sample for the second set of samples is generated based on the model parameters; Determine the difference between the predicted sample and the second group of samples; as well as The cost value associated with the model is determined based on the identified differences.

6. The method of claim 1, wherein determining the corresponding cost value comprises: A first prediction signal associated with the first model is determined based on the model parameters of the first model among the plurality of models; The first cost value in the corresponding cost values ​​is determined based on the difference between the first predicted signal and the second group of samples; A second prediction signal associated with the second model is determined based on the model parameters of the second model among the plurality of models; as well as The second cost value is determined based on the difference between the second predicted signal and the second set of samples.

7. The method of claim 1, wherein the plurality of models includes Local Intensity Compensation (LIC), Linear Model (LM), Convolutional Cross Component Model (CCCM), Gradient and Position-Based CCCM (GL-CCCM), Cross Component Residual Model (CCRM), Block Vector Guided CCCM (BVG-CCCM), and Cross Component Merge (CCMerge).

8. The method of claim 1, wherein determining the model comprises selecting the model associated with the lowest cost value among the respective cost values.

9. The method according to claim 1, further comprising: Construct a candidate model list, wherein the candidates are sorted in the model list based on the corresponding cost values. Determining the model includes selecting the model from the list of candidate models.

10. The method of claim 1, wherein at least one of the plurality of models is a model for encoding or decoding temporally or spatially adjacent blocks of the current block.

11. An apparatus for encoding or decoding video data, the apparatus comprising: One or more memories, the one or more memories being configured to store the video data; and Processing circuitry, coupled to the one or more memories, wherein the processing circuitry is configured to: For each of the multiple models, the corresponding model parameters associated with the corresponding model among the multiple models are derived using the first set of samples; The corresponding cost value associated with the corresponding model among the plurality of models is determined using the corresponding model parameters and the second set of samples, so as to generate the corresponding cost value of the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; The model used for encoding or decoding the current block is determined based on the corresponding cost value; as well as The current block is encoded or decoded based on the determined model.

12. The apparatus of claim 11, wherein, in order to encode or decode the current block, the processing circuitry is configured to encode or decode the current block in an inter-frame prediction mode or an intra-frame prediction mode.

13. The device of claim 11, wherein the second set of samples includes samples immediately adjacent to the current block, and the first set of samples includes samples not immediately adjacent to the current block.

14. The device of claim 11, wherein the second set of samples includes samples immediately adjacent to the current block and samples immediately adjacent to the reference block, and the first set of samples includes samples not immediately adjacent to the current block and samples not immediately adjacent to the reference block.

15. The apparatus of claim 11, wherein, in order to determine the corresponding cost value, the processing circuit is configured to: For each of the plurality of models, a predicted sample for the second set of samples is generated based on the model parameters; Determine the difference between the predicted sample and the second group of samples; as well as The cost value associated with the model is determined based on the identified differences.

16. The device of claim 11, wherein, in order to determine the corresponding cost value, the processing circuit is configured to: A first prediction signal associated with the first model is determined based on the model parameters of the first model among the plurality of models; The first cost value in the corresponding cost values ​​is determined based on the difference between the first predicted signal and the second group of samples; A second prediction signal associated with the second model is determined based on the model parameters of the second model among the plurality of models; as well as The second cost value is determined based on the difference between the second predicted signal and the second set of samples.

17. The device of claim 11, wherein the plurality of models includes Local Intensity Compensation (LIC), Linear Model (LM), Convolutional Cross Component Model (CCCM), Gradient-and-Position-Based CCCM (GL-CCCM), Cross Component Residual Model (CCRM), Block Vector Guided CCCM (BVG-CCCM), and Cross Component Merge (CCMerge).

18. The apparatus of claim 11, wherein, in order to determine the model, the processing circuitry is configured to select the model associated with the lowest cost value among the respective cost values.

19. The apparatus of claim 11, wherein the processing circuit is configured to: Construct a candidate model list, wherein the candidates are sorted in the model list based on the corresponding cost values. In order to determine the model, the processing circuit is configured to select the model from the list of candidate models.

20. One or more computer-readable storage media having instructions stored thereon, the instructions causing one or more processors to: For each of the multiple models, the corresponding model parameters associated with the corresponding model among the multiple models are derived using the first set of samples; The corresponding cost value associated with the corresponding model among the plurality of models is determined using the corresponding model parameters and the second set of samples, so as to generate the corresponding cost value of the plurality of models, wherein the first set of samples and the second set of samples are mutually exclusive; The model used for encoding or decoding the current block is determined based on the corresponding cost value; as well as The current block is encoded or decoded based on the determined model.

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