Video coding and decoding method and related device

Abstract

A method, a computer program and a computer system for encoding video data are provided. Data corresponding to the video frame is received. One or more luma samples and chroma samples are identified from the received image data. Two or more linear models are determined from the identified luma and chroma samples. The luma samples and chroma samples are classified into a number of categories corresponding to the number of linear models, and each category has one or more corresponding linear model parameters that are signaled to the bitstream.

Description

[0001] This application is a divisional application of the Chinese invention patent application whose applicant is Tencent USA LLC, whose filing date is April 12, 2021, whose invention name is “Improved Cross-Component Intra-Frame Prediction Mode”, and whose application number is 202180044407.2. Technical Field

[0002] The present disclosure relates generally to the field of data processing, and more particularly to video encoding and decoding. Background Art

[0003] AOMedia Video 1 (AV1) is an open video coding format designed for video transmission over the Internet. AOMedia Video 1 was developed by the Alliance for Open Media (AOMedia) as the successor to VP9, ​​which was established in 2015 and includes semiconductor companies, video-on-demand providers, video content producers, software development companies, and web browser vendors. AV1 specifies a chroma-from-luma mode that allows chroma-only intra-frame prediction and can model chroma pixels as linear functions of the corresponding reconstructed luma pixels. Summary of the invention

[0004] Embodiments relate to a method, system, and computer-readable medium for encoding video data. According to one aspect, a method for encoding video data is provided. The method may include receiving data corresponding to a video frame. One or more luma samples and chroma samples are identified from the received image data. Two or more linear models are determined from the identified luma samples and chroma samples. The luma samples and chroma samples are classified into a number of categories corresponding to the number of linear models, and each category has one or more corresponding linear model parameters, which are signaled to a bitstream.

[0005] According to another aspect, a computer system for encoding video data is provided. The computer system may include one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one storage device in the one or more storage devices, the program instructions being used to be executed by at least one processor in the one or more processors via at least one storage in the one or more memories, whereby the computer system is capable of performing a method. The method may include receiving data corresponding to a video frame. One or more luminance samples and chrominance samples are identified from the received image data. Two or more linear models are determined from the identified luminance samples and chrominance samples. The luminance samples and chrominance samples are classified into a number of categories corresponding to the number of linear models, and each category has one or more corresponding linear model parameters, which are signaled to a bitstream.

[0006] According to another aspect, a computer-readable medium for encoding video data is provided. The computer-readable medium may include one or more computer-readable storage devices and program instructions stored on at least one tangible storage device in one or more tangible storage devices, and the program instructions can be executed by a processor. The program instructions can be executed by a processor for performing a method, which can accordingly include receiving data corresponding to a video frame. One or more luminance samples and chrominance samples are identified from the received image data. Two or more linear models are determined from the identified luminance samples and chrominance samples. The luminance samples and chrominance samples are classified into a number of categories corresponding to the number of linear models, and each category has one or more corresponding linear model parameters, and the linear model parameters are notified to the bitstream with a signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] These and other objects, features and advantages will become apparent from the following detailed description of illustrative embodiments which is to be read in conjunction with the accompanying drawings. The various features of the drawings are not to scale as these illustrations are intended to facilitate clarity of understanding by those skilled in the art in conjunction with the detailed description. In the drawings:

[0008] Figure 1 illustrates a networked computer environment according to at least one embodiment;

[0009] Figure 2 is a functional block diagram of a process for predicting chrominance components from luma components according to at least one embodiment;

[0010] Figure 3 is an operational flow diagram illustrating steps performed by a program for encoding video data based on multiple linear models according to at least one embodiment;

[0011] Figure 4 According to at least one embodiment Figure 1 A block diagram of the internal and external components of a computer and server depicted in;

[0012] Figure 5 According to at least one embodiment, Figure 1 A block diagram of an illustrative cloud computing environment of a computer system depicted in; and

[0013] Figure 6 According to at least one embodiment Figure 5 A block diagram of the functional layers of an illustrative cloud computing environment. DETAILED DESCRIPTION

[0014] Detailed embodiments of the claimed structures and methods are disclosed herein; however, it is understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be implemented in various forms. However, these structures and methods may be implemented in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Instead, these exemplary embodiments are provided so that the disclosure will be comprehensive and complete and will fully convey the scope to those skilled in the art. In the specification, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the embodiments presented.

[0015] Embodiments relate generally to the field of data processing, and more particularly to video encoding and decoding. The exemplary embodiments described below provide systems, methods, and computer programs for encoding video using, among other things, multiple linear models to predict chrominance components from luma components. Thus, some embodiments have the ability to improve the computational art by enabling improved prediction of chrominance components from luma components by considering luma samples within image boundaries and calculating average luma values ​​based on multiple categories of parameters.

[0016] As previously described, AOMedia Video 1 (AV1) is an open video coding format designed for video transmission over the Internet. AOMedia Video 1 was developed by the Alliance for Open Media (AOMedia) as a successor to VP9, ​​which was established in 2015 and includes semiconductor companies, video on demand providers, video content producers, software development companies, and web browser vendors. AV1 specifies a mode for predicting chroma components from luma components, which allows only chroma intra-frame prediction and can model chroma pixels as linear functions of corresponding reconstructed luma pixels. However, in the mode for predicting chroma components from luma components, only one linear model can be used between luma samples and chroma samples within a complete coding block, but the relationship between luma samples and chroma samples within a complete coding block may not always be well fitted by a single linear model. Additionally, when some samples in a co-located luma block are outside the picture boundary, these samples can be filled and used to calculate the average value of the luma samples, which may result in increased complexity. In addition, when a mode of predicting chroma components from luma components can be selected, all samples in the corresponding luma block can be used to calculate an average luma value, which can be complex when the current block may be equal to or greater than 32 pixels by 32 pixels. Therefore, it may be advantageous to determine one or more linear models for luma samples and chroma samples to improve cross-component intra prediction and reduce complexity costs.

[0017] Various aspects are described herein with reference to flowchart illustrations and / or block diagrams of methods, devices (systems) and computer-readable media according to various embodiments. It will be understood that each block of the flowchart illustration and / or block diagram and the combination of blocks in the flowchart illustration and / or block diagram can be implemented by computer-readable program instructions.

[0018] Now refer to Figure 1 , a functional block diagram of a networked computer environment shows a video frame encoding system 100 (hereinafter referred to as the "system") for encoding video data based on multiple linear models. It should be understood that Figure 1 This only provides an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made based on design and implementation requirements.

[0019] System 100 may include computer 102 and server computer 114. Computer 102 may communicate with server computer 114 via communication network 110 (hereinafter referred to as "network"). Computer 102 may include processor 104 and software program 108 stored on data storage device 106, and computer 102 may interface with a user and communicate with server computer 114. Figure 4 As discussed, computer 102 may include internal components 800A and external components 900A, respectively, and server computer 114 may include internal components 800B and external components 900B, respectively. Computer 102 may be, for example, a mobile device, a phone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing device capable of running programs, accessing a network, and accessing a database.

[0020] The server computer 114 may also operate in a cloud computing service model such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS), as described below with respect to Figure 6 The server computer 114 may also be located in a cloud computing deployment model such as a private cloud, a community cloud, a public cloud, or a hybrid cloud.

[0021] A server computer 114 that can be used to encode video data can run a video encoding program 116 (hereinafter referred to as a "program") that can interact with the database 112. Figure 3 The video encoding program method is described in more detail. In one embodiment, the computer 102 can operate as an input device including a user interface, and the program 116 can run primarily on the server computer 114. In an alternative embodiment, the program 116 can run primarily on one or more computers 102, and the server computer 114 can be used to process and store data used by the program 116. It should be noted that the program 116 can be a stand-alone program, or can be integrated into a larger video encoding program.

[0022] However, it should be noted that in some instances, processing for program 116 may be shared in any ratio between computer 102 and server computer 114. In another embodiment, program 116 may be executed on more than one computer, server computer, or some combination of computers and server computers, such as multiple computers 102 communicating with a single server computer 114 across network 110. In another embodiment, for example, program 116 may be executed on multiple server computers 114 communicating with multiple client computers across network 110. Alternatively, the program may be executed on a network server communicating with the server and multiple client computers across the network.

[0023] The network 110 may include a wired connection, a wireless connection, a fiber optic connection, or some combination thereof. In general, the network 110 may be any combination of connections and protocols that will support communication between the computer 102 and the server computer 114. The network 110 may include various types of networks, such as a local area network (LAN), a wide area network (WAN) such as the Internet, a telecommunication network such as a public switched telephone network (PSTN), a wireless network, a public switched network, a satellite network, a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a metropolitan area network (MAN), a private network, an ad hoc network, an intranet, a fiber-based network, etc., and / or a combination of these or other types of networks.

[0024] Figure 1 The number and arrangement of devices and networks shown in the figure are provided as examples. In practice, there may be Figure 1 The devices and / or networks shown in FIG. 1 may include additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or differently arranged devices and / or networks than those shown in FIG. Figure 1 Two or more of the devices shown in may be implemented in a single device, or Figure 1 The single device shown in can be implemented as multiple distributed devices. Additionally or alternatively, a set of devices (eg, one or more devices) of the system 100 can perform one or more functions described as being performed by another set of devices of the system 100.

[0025] Now refer to Figure 2 , depicts a functional block diagram 200 for predicting chrominance components based on luma components. Predicting chrominance components based on luma components can be expressed as CfL(α)=α×L AC +DC, where L ACα may represent the AC contribution of the luma component, α may represent the parameters of the linear model, and DC represents the DC contribution of the chroma component. The reconstructed luma pixels may be subsampled to the chroma resolution by the subsampling module 202, and the average value determined by the averaging module 204 may be subtracted by the subtraction module 206 to form the AC contribution. In order to approximate the chroma AC component based on the AC contribution, the parameter α is determined based on the original chroma pixels and the parameter α is signaled in the bitstream. The parameter α may be multiplied by the AC contribution by the multiplication module 208. The DC contribution of the chroma component may be calculated using the intra-frame DC mode, and the DC contribution of the chroma component may be added to the output of the multiplication module 208 by the addition module 210.

[0026] Multiple linear models (MLMs) can be used to predict the chrominance component based on the luminance component between the luminance samples and the chrominance samples within a complete coding block, where the number of linear models can be expressed as N, where N>1. When the MLM mode can be selected, the samples within the luminance and chrominance blocks can be classified into N categories, and each category can have its own linear model parameters. For example, the parameter α of the N models can be signaled to the bitstream for each of the models. The samples in the chrominance block can be classified into N models based on their coordinates in the coding block. According to one or more embodiments, samples in even rows or columns can be classified into a first category, and samples in odd rows or columns can be classified into category 1. In one or more embodiments, samples can be classified into two categories by a vertical and an intersecting current block.

[0027] In one or more embodiments, samples in the chrominance block may be classified into N models based on the values ​​of the corresponding reconstructed luma samples. In one or more embodiments, a threshold T1 may be calculated based on the corresponding luma samples, where T1 may be an average or median value of the samples in the corresponding luma block. Each corresponding luma sample may be compared with the threshold T1 to determine the category of the luma sample. For example, when the value of the current luma sample may be less than T1, the corresponding chrominance sample of the luma sample may be classified into the first category. Otherwise, the sample may be classified into the second category.

[0028] In one or more embodiments, the value of the corresponding luma sample may be compared to the DC value to determine the category of the luma sample. The DC value may be the median of a range of pixel values, for example, when the internal bit depth of the codec may be 8, the DC value may be 128, and when the internal bit depth of the codec may be 10, the DC value may be 512. In one or more embodiments, the parameters of the first linear model may be directly signaled, and the difference between the parameter α and the first model may be signaled for the remaining linear models. In one or more embodiments, for the remaining linear models, the difference between the parameter α and the first model may be limited to [-K, K], where K may be a positive integer such as 1 or 2.

[0029] In one or more embodiments, the average luma value for each linear model may be calculated using samples belonging to each individual class. In one or more embodiments, multiple linear models (MLMs) and prediction of chroma components from luma components may be signaled together in one flag, cross component mode (CCM). When the current mode may be CCM, an additional flag may be signaled to indicate whether the current mode may be MLM or prediction of chroma components from luma components. In one or more embodiments, MLM and prediction of chroma components from luma components may be signaled together with nominal angle and non-directional modes.

[0030] When some of the corresponding luma samples may be outside the picture, the luma samples outside the picture may not be filled or used to calculate the average luma value of the mode of predicting the chroma component according to the luma component. In one or more embodiments, all samples and only some samples in the picture may be used to calculate the average luma value of the mode of predicting the chroma component according to the luma component. In one or more embodiments, only the first M rows (and / or N columns) of samples in the picture may be used to calculate the average luma value of the mode of predicting the chroma component according to the luma component, where M or N may be less than or equal to the height or width of the block, and both the values ​​of M and N may be powers of 2.

[0031] When a mode of predicting chrominance components from luma components can be used, an average luma value can be calculated by using samples in one or more predefined positions. In one or more embodiments, after the downsampling process, the luma samples in even / odd rows (or columns) can be used to calculate the average. In one or more embodiments, after the downsampling process, the luma samples in the corner positions and / or the center positions can be used to calculate the average.

[0032] One or more predicted linear model parameters (e.g., slope values ​​and offset values) can be derived using adjacent reconstructed luminance and chrominance samples, and then one or more differential values ​​between the linear model parameters actually used can be signaled. In one or more embodiments, a unary code can be used to signal the differential value. In one or more embodiments, the differential value can be restricted to a predefined range such as [-L, L] so that the calculated differential value can be clamped to -L or L when it may be outside the range.

[0033] Now refer to Figure 3 , depicts an operational flow chart 300 showing steps performed by a program for encoding video data. Figure 3 Can be helped by Figure 1 and Figure 2 As described above, the video encoding program 116 ( Figure 1 ) can encode video quickly and efficiently using prediction of chrominance components based on luma components based on multiple linear models.

[0034] At 302, data corresponding to a video frame is received. The data may be a still image, or may be video data from which one or more frames are extracted. In operation, the server computer 114 ( Figure 1 ) on the video encoding program 116 ( Figure 1 ) can be transmitted through the communication network 110 ( Figure 1 ) from computer 102( Figure 1 ) receives video frame data, or may receive video frame data from database 112 ( Figure 1 ) to retrieve video frame data.

[0035] At 304, one or more luma samples and chroma samples are identified from the received image data. The luma samples and chroma samples may allow intra-frame prediction to enable compression of the video frame data. In operation, the video encoding program 116 ( Figure 1 ) can extract luminance samples and chrominance samples from the received video frame data. The extracted luminance samples and chrominance samples can be stored in the database 112 ( Figure 1 )Inside.

[0036] At 306, two or more linear models are determined from the identified luma samples and chroma samples. The luma samples and chroma samples are classified into a number of categories corresponding to the number of linear models, and each category has one or more corresponding linear model parameters that are signaled to the bitstream. In operation, the video encoding program 116 ( Figure 1 ) can be based on the data stored in the database 112 ( Figure 1 ) in the luminance samples and chrominance samples or the multiplication module 208 ( Figure 2 ) determines one or more linear models based on the luminance component prediction output of the chrominance component.

[0037] Understandably, Figure 3 This merely provides an illustration of one implementation and does not imply any limitation on how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

[0038] Figure 4 According to the illustrative embodiment Figure 1 400 of the internal and external components of a computer. It should be understood that Figure 4 This merely provides an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made based on design and implementation requirements.

[0039] Computer 102 ( Figure 1 ) and server computer 114 ( Figure 1 ) can include Figure 4 800A, 800B and corresponding groups of external components 900A, 900B are shown in FIG. Each group of internal components 800 includes one or more processors 820 on one or more buses 826, one or more computer-readable RAMs (Random Access Memory, RAM) 822 and one or more computer-readable ROMs (Read Only Memory, ROM) 824, one or more operating systems 828, and one or more computer-readable tangible storage devices 830.

[0040] Processor 820 is implemented in hardware, firmware or a combination of hardware and software. Processor 820 is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC) or another type of processing component. In some implementations, processor 820 includes one or more processors that can be programmed to perform functions. Bus 826 includes components that allow communication between internal components 800A and 800B.

[0041] One or more operating systems 828, software programs 108 ( Figure 1 ) and server computer 114 ( Figure 1 ) on the video encoding program 116 ( Figure 1 ) are stored on one or more corresponding computer-readable tangible storage devices 830 for execution by one or more corresponding processors 820 via one or more corresponding RAMs 822 (which typically include cache memory). Figure 4 In the illustrated embodiment, each of the computer-readable tangible storage devices 830 is a disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices 830 is a semiconductor storage device such as ROM 824, EPROM (Erasable Programmable Read-Only Memory, EPROM), flash memory, optical disk, magneto-optical disk, solid state disk, compact disk (Compact Disc, CD), digital versatile disk (Digital Versatile Disk, DVD), floppy disk, cassette tape, magnetic tape and / or another type of non-transitory computer-readable tangible storage device that can store computer programs and digital information.

[0042] Each set of internal components 800A, 800B also includes an R / W drive or interface 832 to read from and write to one or more portable computer readable tangible storage devices 936 such as CD-ROMs (Compact Disc Read-Only Memory, CD-ROMs), DVDs, memory sticks, tapes, disks, optical disks, or semiconductor storage devices. Figure 1 ) and video encoding program 116 ( Figure 1 ) can be stored on one or more corresponding portable computer-readable tangible storage devices 936, read via a corresponding R / W drive or interface 832, and loaded into a corresponding hard disk drive 830.

[0043] Each set of internal components 800A, 800B also includes a network adapter or interface 836 such as a TCP / IP adapter card; a wireless Wi-Fi interface card; or a 3G, 4G or 5G wireless interface card or other wired or wireless communication link. Software Program 108 ( Figure 1 ) and server computer 114 ( Figure 1 ) on the video encoding program 116 ( Figure 1 ) can be downloaded from an external computer to the computer 102 via a network (such as the Internet, a local area network or other, wide area network) and a corresponding network adapter or interface 836 ( Figure 1 ) and server computer 114. The software program 108 and the video encoding program 116 on the server computer 114 are loaded from the network adapter or interface 836 to the corresponding hard disk drive 830. The network may include copper wire, fiber optic, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers.

[0044] Each set of external components 900A, 900B may include a computer display 920, a keyboard 930, and a computer mouse 934. The external components 900A, 900B may also include touch screens, virtual keyboards, touch pads, pointing devices, and other human-computer interface devices. Each set of internal components 800A, 800B also includes a device driver 840 that interfaces with the computer display 920, keyboard 930, and computer mouse 934. The device driver 840, R / W drive or interface 832, and network adapter or interface 836 include hardware and software (stored in storage device 830 and / or ROM 824).

[0045] It is understood in advance that although the present disclosure includes detailed descriptions about cloud computing, the implementation of the teachings described herein is not limited to a cloud computing environment. Instead, some embodiments can be implemented in conjunction with any other type of computing environment now known or later developed.

[0046] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a service provider. The cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

[0047] Features are as follows:

[0048] On-demand self-service: Cloud consumers can unilaterally and automatically provision computing capabilities such as server time and network storage as needed without manual interaction with the service provider.

[0049] Broad network access: Capabilities are available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin-client or thick-client platforms (e.g., mobile phones, laptops, and PDAs).

[0050] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated based on demand. There is a sense of location independence because consumers generally have no control or knowledge of the exact location of the resources provided, but may be able to specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0051] Rapid elasticity: Capacity can be provisioned quickly and elastically (in some cases automatically) to quickly scale out, and released quickly to quickly scale in. To the consumer, the capacity available for provisioning often appears to be unlimited and can be purchased at any time and in any quantity.

[0052] Metered Services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be determined, controlled, and reported, providing transparency to both the provider and consumer of the utilized service.

[0053] The service model is as follows:

[0054] Software as a Service (SaaS): The capability provided to consumers is to use the provider's applications running on a cloud infrastructure. Applications can be accessed from a variety of client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0055] Platform as a Service (PaaS): The capability provided to consumers is to deploy consumer-created or acquired applications created using programming languages ​​and tools supported by the provider onto the cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possible configuration of the application hosting environment.

[0056] Infrastructure as a Service (IaaS): The capabilities provided to consumers are processing, storage, networking, and other basic computing resources on which consumers can deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but have control over the operating system, storage, deployed applications, and possibly limited control over selected networking components (e.g., host firewalls).

[0057] The deployment model is as follows:

[0058] Private cloud: Cloud infrastructure is operated only for an organization. Cloud infrastructure can be managed by the organization or a third party and can exist locally (on-premises) or externally (off-premises).

[0059] Community cloud: The cloud infrastructure is shared by several organizations and supports a specific community with shared concerns (e.g., mission, security requirements, policies, and compliance considerations). The cloud infrastructure can be managed by the organization or a third party and can exist on-premises or off-premises.

[0060] Public cloud: Cloud infrastructure available to the general public or large industry groups and owned by an organization that sells cloud services.

[0061] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds).

[0062] Cloud computing environments are service-oriented, with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the core of cloud computing is the infrastructure that consists of a network of interconnected nodes.

[0063] Reference Figure 5, depicts an illustrative cloud computing environment 500. As shown, the cloud computing environment 500 includes one or more cloud computing nodes 10, and local computing devices used by cloud consumers, such as personal digital assistants (PDAs) or cellular phones 54A, desktop computers 54B, laptop computers 54C, and / or automotive computer systems 54N, can communicate with one or more cloud computing nodes 10. The cloud computing nodes 10 can communicate with each other. The cloud computing nodes 10 can be physically or virtually grouped (not shown) into one or more networks, such as a private cloud, community cloud, public cloud, or hybrid cloud, or a combination thereof, as described above. This allows the cloud computing environment 500 to provide infrastructure, platforms, and / or software as a service, and cloud consumers do not need to maintain resources on local computing devices for the service. It should be understood that Figure 5 The types of computing devices 54A- 54N shown in are intended to be illustrative only, and cloud computing node 10 and cloud computing environment 500 may communicate with any type of computerized device over any type of network and / or network-addressable connection (eg, using a web browser).

[0064] Reference Figure 6 , showing a cloud computing environment 500 ( Figure 5 ) provides a set of functional abstraction layers 600. It should be understood in advance that Figure 6 The components, layers, and functions shown in are intended to be illustrative only, and the embodiments are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0065] The hardware and software layer 60 includes hardware components and software components. Examples of hardware components include: mainframes 61; servers based on RISC (Reduced Instruction Set Computer) architecture 62; servers 63; blade servers 64; storage devices 65; and network and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0066] Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual servers 71 ; virtual storage 72 ; virtual networks 73 including virtual private networks; virtual applications and operating systems 74 ; and virtual clients 75 .

[0067] In one example, the management layer 80 may provide the functionality described below. Resource provisioning 81 provides dynamic acquisition of computing resources and other resources for performing tasks within a cloud computing environment. Metering and pricing 82 provides cost determination when utilizing resources within a cloud computing environment, as well as bills or invoices for consumption of these resources. In one example, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management so that the required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provides pre-scheduling and procurement of cloud computing resources, anticipating future demand for the cloud computing resources based on the SLA.

[0068] Workload layer 90 provides examples of functionality for which a cloud computing environment may be utilized. Examples of workloads and functionality that may be provided from this layer include: drawing and navigation 91; software development and lifecycle management 92; virtual classroom instruction delivery 93; data analytics processing 94; transaction processing 95; and video encoding 96. Video encoding 96 may encode video data based on multiple linear models for predicting chrominance components from luma components.

[0069] Some embodiments may involve systems, methods, and / or computer-readable media at any possible level of integrated technical detail. A computer-readable medium may include a computer-readable non-transitory storage medium (or multiple media) having computer-readable program instructions thereon for causing a processor to perform operations.

[0070] A computer-readable storage medium may be a tangible device capable of retaining and storing instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: a portable computer disk, a hard disk, a random access memory (Random Access Memory, RAM), a read-only memory (Read-Only Memory, ROM), an erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM or flash memory), a static random access memory (Static Random Access Memory, SRAM), a portable compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), a digital versatile disk (Digital Versatile Disk, DVD), a memory stick, a floppy disk, a mechanical encoding device such as a punch card or a raised structure in a groove on which instructions are recorded, and any suitable combination of the foregoing. Computer-readable storage media as used herein should not be construed as being themselves transient signals such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0071] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or downloaded to an external computer or external storage device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network, and forwards the computer-readable program instructions to be stored in a computer-readable storage medium in the corresponding computing / processing device.

[0072] The computer readable program code / instruction for performing the operation can be an assembly instruction, an instruction set architecture (Instruction-Set-Architecture, ISA) instruction, a machine instruction, a machine-related instruction, a microcode, a firmware instruction, a state setting data, a configuration data of an integrated circuit system, or a source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc. and process programming languages ​​such as "C" programming language or similar programming languages. The computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of executing completely on a remote computer or server, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN) or can be connected to an external computer (e.g., by using the Internet of an Internet service provider). In some embodiments, an electronic circuit system including, for example, a programmable logic circuit system, a field programmable gate array (FPGA), or a programmable logic array (PLA) can execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit system to perform various aspects or operations.

[0073] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create a device for implementing the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide the computer, programmable data processing device, and / or other equipment to operate in a specific manner, so that the computer-readable storage medium having the instructions stored therein includes an article of manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0074] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps executed on the computer, other programmable apparatus, or other device produces a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions and operations of possible implementations of systems, methods and computer-readable media according to various embodiments. In this regard, each block in the flowchart or block diagram may represent a module, a segment or a portion of an instruction, which includes one or more executable instructions for implementing a specific logical function. The method, computer system and computer-readable media may include additional blocks, fewer blocks, different blocks or blocks of different arrangements compared to those blocks depicted in the accompanying drawings. In some alternative implementations, the functions indicated in the blocks may not occur in the order indicated in the figure. For example, the two blocks shown in succession may actually be performed simultaneously or substantially simultaneously, or the blocks may sometimes be performed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart illustration and the combination of the blocks in the block diagram and / or flowchart illustration may be implemented by a hardware-based dedicated system that performs a specific function or action or realizes a combination of dedicated hardware and computer instructions.

[0076] It will be apparent that the systems and / or methods described herein can be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit the implementation. Therefore, the operation and behavior of the systems and / or methods are described herein without reference to specific software code - it should be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0077] Unless explicitly described as such, the elements, actions or instructions used herein should not be interpreted as critical or necessary. In addition, as used herein, the articles "a kind of" and "an" are intended to include one or more items, and can be used interchangeably with "one or more". In addition, as used herein, the term "group" is intended to include one or more items (e.g., related items, unrelated items, combinations of related items and unrelated items, etc.), and can be used interchangeably with "one or more". In the case of only one item being meant, the term "one" or similar language is used. In addition, as used herein, the terms "have", "have", "with", etc. are intended to be open terms. In addition, unless otherwise expressly stated, the phrase "based on" is intended to mean "based at least in part on".

[0078] The description of various aspects and embodiments has been presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Even if combinations of features are described in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features can be combined in ways that are not specifically described in the claims and / or disclosed in the specification. Although each dependent claim listed in the attached claims may directly refer to only one claim, the disclosure of possible implementations includes the combination of each dependent claim with each other claim in the claim set. Without departing from the scope of the described embodiments, many modifications and changes will be obvious to those of ordinary skill in the art. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or technical improvements to the technologies found in the market, or to enable other ordinary technicians in the art to understand the embodiments disclosed herein.

Claims

1. A video encoding method, characterized in that: The method comprises: identifying from the image data one or more luma samples and one or more chroma samples, the one or more chroma samples being predicted from the one or more luma samples based on a DC contribution of a chroma component of the image data and an AC contribution of a luma component of the image data; and Two or more linear models are determined from the identified one or more luma samples and the identified one or more chroma samples, wherein the one or more luma samples and the one or more chroma samples are classified into categories corresponding to the number of linear models and each category has one or more corresponding linear model parameters, which are signaled in the bitstream.

2. The method according to claim 1, characterized in that The one or more chroma samples are classified into categories corresponding to one or more models based on coordinates associated with the one or more chroma samples.

3. The method according to claim 2, characterized in that Chroma samples corresponding to even-numbered rows or columns are classified into a first category, and chroma samples corresponding to odd-numbered rows or columns are classified into a second category.

4. The method according to claim 1, characterized in that: The one or more chrominance samples are classified into the linear model based on one or more values ​​of corresponding reconstructed luma samples.

5. The method according to claim 1, characterized in that Parameters of a first one of the linear models are directly signaled, and differences between one or more of the parameters and the first linear model are signaled for the remaining linear models.

6. The method according to claim 1, characterized in that One or more mean brightness values ​​for each linear model are calculated by using samples belonging to each of the categories.

7. The method according to claim 1, characterized in that The first flag signals that the linear model and the state corresponding to the mode of predicting chroma components from luma components is in cross-component mode, and the second flag signals whether the current mode corresponds to the linear model or the mode of predicting chroma components from luma components.

8. The method according to claim 1, characterized in that One or more luma samples outside of a boundary associated with the image data are not used to calculate an average luma value for a mode of predicting chroma components from luma components.

9. The method according to claim 1, characterized in that: When the predict chroma component from luma component mode is used, one or more average luma values ​​are calculated by using one or more luma samples in one or more predefined positions.

10. The method according to any one of claims 1 to 9, characterized in that Adjacent reconstructed luma and chroma samples are used to derive one or more predicted linear model parameters.

11. A video encoding method, characterized in that: The method comprises: identifying at least one luma sample and at least one chroma sample from the image data, wherein the at least one chroma sample is predicted from the at least one luma sample based on a direct current (DC) contribution of a chroma component of the image data and an alternating current (AC) contribution of a luma component of the image data, Therein, the AC contribution of the luminance component is determined based on the reconstructed luminance pixel sub-sampling and at least one average luminance value.

12. The method according to claim 11, characterized in that The method further comprises: A plurality of linear models are determined from the identified at least one luma sample and the identified at least one chroma sample, wherein the at least one chroma sample is predicted from the at least one luma sample based on parameters of one of the plurality of linear models.

13. The method according to claim 12, characterized in that Each of the plurality of linear models corresponds to a category, The at least one luma sample and the at least one chroma sample are classified into one of the plurality of categories, and Each of the plurality of categories has at least one corresponding linear model parameter.

14. The method according to claim 13, characterized in that Each chrominance sample is classified based on the coordinates associated with it.

15. The method according to claim 13, characterized in that Each chrominance sample is classified based on the value of the corresponding reconstructed luma sample.

16. The method according to claim 13, characterized in that The AC contribution of the luma component is also determined based on a plurality of average luma values, each average luma value corresponding to one of the plurality of categories and determined based on at least one luma sample classified into the corresponding category.

17. The method according to claim 11, characterized in that At least one luminance sample outside of a boundary associated with the image data is omitted when determining the at least one average luminance value.

18. The method according to claim 11, characterized in that The at least one average brightness value is determined based on brightness samples of at least one predefined position in the image data.

19. The method according to claim 11, wherein: The at least one chroma sample is also predicted from the at least one luma sample based on parameters of a linear model between the luma sample and the chroma samples.

20. A method for video decoding, characterized in that: The method comprises: identifying from the image data one or more luma samples and one or more chroma samples, the one or more chroma samples being predicted from the one or more luma samples based on a DC contribution of a chroma component of the image data and an AC contribution of a luma component of the image data; and Two or more linear models are determined from the identified one or more luma samples and the identified one or more chroma samples, wherein the one or more luma samples and the one or more chroma samples are classified into categories corresponding to the number of linear models, and each category has one or more corresponding linear model parameters, and the linear model parameters are parsed from the bitstream.

21. A video decoding method, characterized in that: The method comprises: identifying at least one luma sample and at least one chroma sample from the image data, wherein the at least one chroma sample is predicted from the at least one luma sample based on a direct current (DC) contribution of a chroma component of the image data and an alternating current (AC) contribution of a luma component of the image data, The AC contribution of the luminance component is determined based on the reconstructed luminance pixel sub-sampling and at least one average luminance value.

22. A computer device, the computer device comprising: one or more computer-readable non-transitory storage media configured to store computer program code; as well as One or more computer processors are configured to access the computer program code and execute the method according to any one of claims 1-21 as instructed by the computer program code.

23. A non-transitory computer-readable medium having stored thereon a computer program for encoding video data, the computer program being configured to cause one or more computer processors to perform the method according to any one of claims 1-21.

24. A method for processing a video bitstream, characterized in that: The video bit stream is generated according to the encoding method according to any one of claims 1 to 19, or the video bit stream is decoded according to the decoding method according to any one of claims 20 to 21.

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