Video data decoding method, computer system, and storage medium

Abstract

The present disclosure provides a video data decoding method, a computer system and a storage medium. Video data is received, and a set of hybrid transform kernels corresponding to the video data is identified. A subset of hybrid transform kernels is selected from the set of hybrid transform kernels explicitly or implicitly. The video data is decoded based on the selected subset of hybrid transform kernels.

Description

[0001] Cross Reference to Related Applications

[0002] This application claims priority to U.S. Patent Application No. 17 / 066,791 filed October 9, 2020, which claims priority to U.S. Provisional Application No. 63 / 032,216 filed May 29, 2020. The entire contents of the prior applications are incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates generally to the field of data processing, and more specifically to video encoding and decoding. BACKGROUND

[0004] AOMedia Video 1 (AV1) is an open video coding format designed for video transmission over the Internet. It was developed by the Alliance for Open Media (AOMedia), a consortium founded in 2015 that includes semiconductor companies, video on demand providers, video content producers, software developers, and web browser vendors. Many of the components of the AV1 project originated in previous research efforts by Alliance members. Individual contributors began experimenting with technology platforms several years ago: Xiph's / Mozilla's Daala had code published in 2010, Google's experimental VP9 evolution project VP10 was published on September 12, 2014, and Cisco's Thor was published on August 11, 2015. Building on the codebase of VP9, AV1 incorporates additional technologies, several of which were developed in these experimental formats. The first version of the AV1 reference codec, 0.1.0, was published on April 7, 2016. The Alliance announced the release of the AV1 bitstream specification, as well as a software-based reference encoder and decoder, on March 28, 2018. On June 25, 2018, a confirmed version 1.0.0 of the specification was released. On January 8, 2019, a confirmed version 1.0.0 of the specification was released, including Errata 1. The AV1 bitstream specification includes a reference video codec. SUMMARY

[0005] Embodiments relate to methods, systems, and computer readable media for decoding video data. According to one aspect, a method of video data decoding is provided. The method can include receiving video data. A set of hybrid transform kernels corresponding to the video data is identified. A subset of hybrid transform kernels is explicitly or implicitly selected from the set of hybrid transform kernels. The video data is decoded based on the selected subset of hybrid transform kernels.

[0006] According to another aspect, a computer system for decoding video data is provided. The computer system can 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 of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method. The method can include receiving video data. Identifying a set of hybrid transform kernels corresponding to the video data. Explicitly or implicitly selecting a subset of hybrid transform kernels from the set of hybrid transform kernels. Decoding the video data based on the selected subset of hybrid transform kernels.

[0007] According to yet another aspect, a computer-readable medium for decoding video data is provided. The computer-readable medium can include one or more computer-readable storage devices, and program instructions stored on at least one of the one or more tangible storage devices, the program instructions executable by a processor. The program instructions can be executable by the processor to perform a method. The method can correspondingly include receiving video data. Identifying a set of hybrid transform kernels corresponding to the video data. Explicitly or implicitly selecting a subset of hybrid transform kernels from the set of hybrid transform kernels. Decoding the video data based on the selected subset of hybrid transform kernels. BRIEF DESCRIPTION OF DRAWINGS

[0008] These and other objects, features, and advantages will become apparent in light of the following detailed description of illustrative embodiments thereof, read in conjunction with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity in explaining the illustrative embodiments to a person skilled in the art. In the drawings:

[0009] Figure 1 A networked computer environment is shown in accordance with at least one embodiment;

[0010] Figure 2 is an exemplary line graph transform in accordance with at least one embodiment;

[0011] Figure 3 is an operational flow diagram of steps performed by a program for decoding video data based on implicit or explicit selection of hybrid transform kernels in accordance with at least one embodiment;

[0012] Figure 4 is a block diagram of internal and external components of a computer and server depicted in Figure 1

[0013] Figure 5 is a block diagram of internal and external components of a computer and server depicted in Figure 1 ​a block diagram of illustrative functional layers of a cloud computing environment in

[0014] Figure 6 is a block diagram of an illustrative cloud computing environment of the computer system depicted in Figure 5 a block diagram of illustrative functional layers of a cloud computing environment in DETAILED DESCRIPTION

[0015] Detailed embodiments of the claimed structures and methods are disclosed herein. It will be appreciated, however, that the disclosed embodiments are merely examples of the claimed structures and methods that can be embodied in various forms. These structures and methods can be implemented in many different forms and should not be construed as limited to the examples set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope to those skilled in the art. In the description, well-known features and techniques can be omitted or simplified to avoid obscuring the presented embodiments.

[0016] Embodiments generally relate to the field of data processing, and more particularly to video encoding and decoding. The exemplary embodiments described below provide a system, method, and computer program that includes encoding and decoding video data based on, implicitly or explicitly, selecting a hybrid transform kernel. Thus, some embodiments have the ability to improve the field of computing by using hybrid transform kernels to improve coding efficiency, where the hybrid transform kernel is specified by a computer from video data.

[0017] As previously mentioned, AOMedia Video 1 (AV1) is an open video coding format designed for video transmission over the Internet. It was developed by the Alliance for Open Media (AOMedia) as a successor to VP9, a consortium founded in 2015 that includes semiconductor companies, video on demand providers, video content producers, software development companies, and web browser vendors. Many of the components of the AV1 project stem from previous research efforts by Alliance members. Individual contributors began experimenting with technology platforms several years ago: Xiph's / Mozilla's Daala had code published in 2010, Google's experimental VP9 evolution project VP10 was published on September 12, 2014, and Cisco's Thor was published on August 11, 2015. Building on the codebase of VP9, AV1 incorporates additional technologies, several of which were developed in these experimental formats. The first version of the AV1 reference codec, 0.1.0, was published on April 7, 2016. The Alliance announced the release of the AV1 bitstream specification, as well as a software-based reference encoder and decoder, on March 28, 2018. On June 25, 2018, a confirmed version 1.0.0 of the specification was released. On January 8, 2019, a confirmed version 1.0.0 of the specification was released, including Errata 1. The AV1 bitstream specification includes a reference video codec.

[0018] Unlike VP9, which has only one transform type per coding block, AV1 allows each transform block to independently choose its own transform kernel. AV1 utilizes a set of hybrid transform kernels to code intra prediction residuals. Hybrid transform kernels generally refer to 2-D separable transform kernels that are a combination of various 1-D kernels such as DCT, ADST, flipped ADST (FLIP ADST), and identity transform (IDTX). The set of hybrid transform kernels and their availability for luma intra prediction residuals depend on the size of the residual block. For chroma intra prediction residuals, the transform type is implicitly selected according to the intra prediction mode. However, with the introduction of LGTs (and their flipped versions) and KLTs in the AV2 development process, the set of available hybrid transform kernels for coding luma and chroma intra prediction residuals has expanded. The particular hybrid transform type is selected from this expanded set and signaled in the bitstream for each residual coding block incurs additional computational complexity and bit rate overhead. It is therefore advantageous to use the intra mode dependent and residual block size dependent LGTs and KLTs in an implicit manner to exploit the directional nature of the residual size variation to improve coding performance while reducing computational complexity and bit rate overhead.

[0019] Aspects are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products in accordance with various embodiments. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer readable program instructions.

[0020] Reference is now made to the description of the functional block diagram of a networked computer environment for a video coding system 100 (hereinafter "system") that encodes and decodes video data based on implicitly or explicitly selected hybrid transform kernel, with reference to Figure 1 , it should be appreciated that the depicted environment is provided merely for purposes of illustration and without any limitation, Figure 1 as the different embodiments can be implemented in a variety of environments. Numerous modifications to the depicted environments will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments and environments without departing from the scope of the claims.

[0021] The system 100 can include a computer 102 and a server computer 114. The computer 102 can communicate with the server computer 114 via a communication network 110 (hereinafter "network"). The computer 102 can include a processor 104 and software programs 108 stored on a data storage device 106, and be capable of interfacing with a user and communicating with the server computer 114. As will be discussed below with reference to Figure 4 , the computer 102 can include internal components 800A and external components 900A, respectively, and the server computer 114 can include internal components 800B and external components 900B, respectively. The computer 102 can 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 a program, accessing a network and accessing a database.

[0022] The server computer 114 can 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 (laaS), as discussed below with reference to Figure 5 and Figure 6 The server computer 114 can also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

[0023] The server computer 114, which can decode video data based on implicitly or explicitly selected hybrid transform kernel, can run a video decoding program 116 (hereinafter "program") that can interact with a database 112. Reference is made to the description below with reference to Figure 3The video decoding procedure method will be explained in more detail below. In one embodiment, computer 102 may operate as an input device including a user interface, while program 116 may run primarily on server computer 114. In an alternative embodiment, program 116 may run primarily on one or more computers 102, while server computer 114 may be used to process and store data used by program 116. It should be noted that program 116 may be a standalone program or may be integrated into a larger video decoding program.

[0024] However, it should be noted that in some cases, the processing of program 116 can be shared between computer 102 and server computer 114 at any ratio. In another embodiment, program 116 can run on more than one computer, server computer, or some combination of computers and server computers, for example, multiple computers 102 communicating with a single server computer 114 via network 110. In another embodiment, for example, program 116 can run on multiple server computers 114, wherein the multiple server computers 114 communicate with multiple client computers via network 110. Alternatively, the program can run on a network server, wherein the network server communicates with a server and multiple client computers via a network.

[0025] Network 110 may include wired connections, wireless connections, fiber optic connections, or some combination thereof. Typically, network 110 may be any combination of connections and protocols supporting communication between computer 102 and server computer 114. Network 110 may include various types of networks, such as local area networks (LANs), wide area networks (WANs) such as the Internet, telecommunications networks such as public switched telephone networks (PSTNs), wireless networks, public switched networks, satellite networks, cellular networks (e.g., fifth-generation (5G) networks, long-term evolution (LTE) networks, third-generation (3G) networks, code division multiple access (CDMA) networks, etc.), public land mobile networks (PLMNs), metropolitan area networks (MANs), private networks, ad hoc networks, intranets, fiber optic-based networks, etc., and / or combinations of these or other types of networks.

[0026] Figure 1 The number and arrangement of devices and networks shown are provided as an example. In reality, with...Figure 1 There can be more, fewer, or a different arrangement of devices and / or networks than those shown, which are merely examples. In addition, Figure 1 Two or more of the devices shown can be implemented within a single device, or Figure 1 A single device shown can be implemented as multiple, distributed devices. Additionally or in the alternative, a group of devices (e.g., one or more devices) of system 100 can perform one or more functions described as being performed by another group of devices of system 100.

[0027] Referring now to Figure 2 , an example Line Graph Transform (LGT) 200 is depicted. Graphs can be a general mathematical structure consisting of a set of vertices and edges used to model relationships of interest between objects. In fact, weighted graphs (assigning a set of weights to edges and possibly to vertices) can provide a sparse representation for robust modeling of signals / data. LGTs can improve coding efficiency by better adapting to different block statistics. By learning a line graph from data to model the underlying row- and column-wise statistics of a block residual signal, separable LGTs can be designed and optimized, where the associated Generalized Graph Laplacian (GGL) matrix is used to derive the LGT.

[0028] For example, given a weighted graph G(W, V), the GGL matrix can be defined as L c = D - W + V, where W can be an adjacency matrix consisting of non-negative edge weights w c , D can be a diagonal degree matrix, and V can be a diagonal matrix representing weighted self-loops v c1 , v c2 . The matrix L c can be represented as:

[0029]

[0030] The LGT can then be derived by eigen-decomposition of the GGL L c . The LGT L c = UΦU Twhere the columns of the orthogonal matrix U are the basis vectors of the LGT and Φ is a diagonal eigenvalue matrix. In fact, the Discrete Cosine Transforms (DCT) and Discrete Sine Transforms (DST), including DCT-2, DCT-8 and DST-7, are LGTs derived from certain forms of GGL. By setting v c1 = 0, DCT-2 is derived. By setting v c1 = w c , DST-7 is derived. By setting v c2 = w c , DCT-8 is derived. By setting v c1 = 2w c , DST-4 is derived. By setting v c2 = 2w c , DCT-4 is derived.

[0031] The LGT is implemented using matrix multiplication with transform sizes of 4, 8 and 16. By setting v c = 2w c1 in the L c 4-point LGT kernel is derived, which means it is DST-4. By setting v c = 1.5w c1 in the L c 8-point LGT kernel is derived, and by setting v c = w c1 in the L c 16-point LGT kernel is derived, which means it is DST-7.

[0032] An expanded set of hybrid transform kernels can be referred to as set A. Set A exhaustively includes all combinations of Discrete Cosine Transform (DCT), Identity Transform (IDTX, skipping transform coding in a certain direction), Asymmetric Discrete Sine Transform (ADST), flipped asymmetric discrete sine transform (FLIP ADST, applying ADST in reverse order), Line Graph Transform (LGT), flipped line graph transform (FLIP LGT), Karhunen-Loeve Transform (KLT), etc. A subset of elements of A that can be a reduced set of transform types can be referred to as x, such that x e A. Subset x can include one or more transform types (e.g., DCT, ADST, LGT, KLT), and / or, one or more combinations of vertical and horizontal transform types (e.g., DCT_DCT, LGT_LGT, DCT_LGT, LGT_DCT).

[0033] According to one or more embodiments, an implicit method can be used to select elements of x, such that hybrid transform types can be selected based on coded information available to both the encoder and the decoder. Thus, no additional signaling is needed to specify transform types at the decoder. In one embodiment, selection can be made according to intra prediction modes and / or block sizes. In one embodiment, one or more of 8 nominal modes, 5 non-angular smoothing modes, and an angle delta value (e.g., -3~+3) can be considered during the selection process. In one embodiment, for directional intra prediction modes, only nominal mode selection of transform types can be used (i.e., directional intra prediction modes that share the same nominal mode but different angle delta values can apply the same implicit transform type).

[0034] In one embodiment, the same hybrid transform type is selected for recursive filtering mode and DC mode. In one embodiment, the same hybrid transform type is selected for recursive filtering mode and SMOOTH mode. In one embodiment, the same hybrid transform type is selected for SMOOTH, SMOOTH H, SMOOTH V modes. In one embodiment, the same hybrid transform type is selected for SMOOTH, SMOOTH H, SMOOTH V and Paeth prediction mode. In one embodiment, the same hybrid transform type is selected for recursive filtering mode, SMOOTH and Paeth prediction mode. In one embodiment, the same hybrid transform type is selected for Vertical mode, V_PRED and SMOOTH V prediction mode. In one embodiment, the same hybrid transform type is selected for Horizontal mode, H_PRED and SMOOTH H prediction mode. In one embodiment, the same hybrid transform type is selected for CfL mode and DC mode. In one embodiment, the same hybrid transform type is selected for CfL mode and SMOOTH mode. In one embodiment, the same hybrid transform type is selected for CfL mode and Paeth mode.

[0035] In one embodiment, LGT with different auto-loop weights (v c1 ,v c2 ) can be used according to one or more of 8 nominal modes, 5 non-angular smoothing modes, angledelta values (e.g., -3 to +3) and block sizes. In one embodiment, KLT with different statistical properties can be used according to one or more of 8 nominal modes, 5 non-angular smoothing, angledelta values (e.g., -3 to +3) and block sizes. In one embodiment, the same intra prediction modes can be enabled for luma and chroma components, and the implicit hybrid transform selection can be the same.

[0036] According to one or more embodiments, an explicit method can be proposed to select the elements of x, which can require syntax signaled in the bitstream to identify the selection (i.e., the encoder needs to explicitly select and signal the transform type at the block-level). The block-level can include a superblock level, a coding block level, a prediction block level, or a transform block level. In one embodiment, an explicit transform scheme (at least two transform type candidates) can be applied for all intra prediction modes, but the number of hybrid transform candidates can be different for different intra prediction modes. In another embodiment, for some intra prediction modes, either an implicit or explicit transform scheme can be used, while other intra prediction modes apply an implicit transform scheme (only one transform type is available). In one embodiment, when the explicit transform scheme involves the use of LGT, an identifier of the self-loop weight (v c1 、v c2 ) of the specified LGT candidate can be signaled in the bitstream at the block-level, which can be an index or a self-loop rate value of the associated self-loop rate value. In one embodiment, when the explicit transform scheme involves the use of KLT, an identifier of the KLT kernel can be signaled in the bitstream at the block-level, which can be an index or a KLT matrix element value of the KLT.

[0037] The switching between the explicit method and the implicit method can be indicated at a high-level syntax or a block-level. When the selection can be indicated at the HLS, it can include a Video Parameter Set (VPS), a Sequence Parameter Set (SPS), a Picture Parameter Set (PPS), a Slice Header. When the switching can be indicated at the block-level, it can include a superblock level, a coding block level, a prediction block level, and / or, a transform block level.

[0038] Referring now to the drawing Figure 3 , an operational flow diagram illustrating steps of a method 300 of decoding video data is depicted. In some implementations, one or more of the process blocks in Figure 3 may be performed by the computer 102 Figure 1 ) and the server computer 114 Figure 1 ). In some implementations, one or more of the process blocks in Figure 3 may be performed by another device or a group of devices independent of, or including, the computer 102 and the server computer 114.

[0039] At 302, the method 300 includes receiving video data.

[0040] At 304, method 300 includes identifying a set of hybrid transform kernels corresponding to the video data.

[0041] At 306, method 300 includes explicitly or implicitly selecting a subset of hybrid transform kernels from a set of hybrid transform kernels. In one specific implementation, the subset of hybrid transform kernels is implicitly selected. The subset of hybrid transform kernels can also be selected based on at least one of an intra-prediction mode and a block size associated with the received video data. The intra-prediction modes include one or more of DC mode, SMOOTH mode, SMOOTH_H horizontal smoothing mode, SMOOTH_V vertical smoothing mode, V_PRED vertical mode, H_PRED horizontal mode, chroma-from-luma, and Paeth. The subset of hybrid transform kernels can also be explicitly selected. The subset of hybrid transform kernels is identified by a syntax element that signals the selection in the bitstream associated with the video data. An explicit transform scheme can be applied to all intra-prediction modes. The number of hybrid transform candidates differs for different intra-prediction modes. The method may further include applying an explicit transform scheme to a subset of intra-prediction modes. A subset of the hybrid transform kernel can be switched between explicit and implicit based on signaling at the high-level syntax or block level.

[0042] At 308, method 300 includes decoding the video data based on a subset of selected hybrid transform kernels.

[0043] Understandable Figure 3 This is merely an illustration of one implementation and does not imply any limitations on how different embodiments can be implemented. Many modifications can be made to the depicted environment to suit design and implementation requirements.

[0044] Figure 4 According to the illustrative embodiments Figure 1 The block diagram 400 depicts the internal and external components. It should be understood that... Figure 4 This is merely an illustration of one implementation and does not imply any limitation on the environments in which different embodiments can be implemented. Many modifications can be made to the depicted environment to suit design and implementation requirements.

[0045] Computer 102 ( Figure 1 ) and server computer 114 ( Figure 1 ) can include Figure 4The illustrated sets of internal components 800A, 800B and external components 900A and 900B. Each set of internal components 800 includes one or more processors 820, one or more computer-readable random access mediums (RAM) 822, and one or more computer-readable read only memories (ROM) 824 on one or more buses 826, one or more operating systems 828, and one or more computer-readable tangible storage devices 830.

[0046] The processor 820 is implemented in hardware, firmware, or a combination of hardware and software. The 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 embodiments, the processor 820 includes one or more processors capable of being programmed to perform a function. The bus 826 includes a component that permits communication among the internal components 800A and 800B.

[0047] The one or more operating systems 828, software programs 108( Figure 1 ) and video decoding programs 116( Figure 1 ) on the server computer 114( Figure 1 ) are stored in respective one or more computer-readable tangible storage devices 830 for execution by one or more respective processors 820 via one or more respective RAMs 822 (which typically include cache memory). In Figure 4 In the illustrated embodiment, each computer-readable tangible storage device 830 is a magnetic disk storage of an internal hard drive. Alternatively, each computer-readable tangible storage device 830 is a semiconductor memory device, such as a ROM 824, EPROM, flash memory, optical disk, magneto-optical disk, solid-state disk, compact disk (CD), digital versatile disk (DVD), floppy disk, tape, magnetic strip, and / or other types of non-transitory computer- readable tangible storage device(s) that can store data and / or computer program code.

[0048] Each set of internal components 800A, 800B also includes a Read / Write (R / W) drive or interface 832 to read from and write to one or more portable computer-readable tangible storage devices 936 such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. Software programs such as the software programs 108( Figure 1 ) and video decoding programs 116( Figure 1 ) can be stored on one or more of the respective portable computer- readable tangible storage devices 936, read via the respective R / W drive or interface 832 and loaded into the respective hard drive 830.

[0049] Each set of internal components 800A, 800B also includes a network adapter or interface 836 such as a TCP / IP adapter card; wireless Wi-Fi interface card; or 3G, 4G, or 5G wireless interface cards or other wired or wireless communication links. The software programs 108( Figure 1 ) and video decoding programs 116( Figure 1 ) on the server computer 114( Figure 1 ) can be downloaded to the computer 102( Figure 1 ) and server computer 114 from an external computer via a network (e.g. the Internet, a local area network or other wide area network) and respective network adapter or interface 836. From the network adapter or interface 836, the software programs 108 and video decoding programs 116 on the server computer 114 are loaded into the respective hard drive 830. The network can comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers.

[0050] Each set of external components 900A, 900B can include a computer display monitor 920, a keyboard 930, and a computer mouse 934. External components 900A, 900B can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each set of internal components 800A, 800B also includes device drivers 840 to interface to computer display monitor 920, keyboard 930 and computer mouse 934. Device drivers 840, R / W drive or interface 832 and network adapter or interface 836 comprise hardware and software (stored in storage device 830 and / or ROM 824).

[0051] It is to be preliminarily understood that, although the present disclosure includes a detailed description of cloud computing, implementation of the teachings described herein are not limited to a cloud computing environment. Rather, some embodiments are capable of functioning with any other type of computing environment now known or later developed.

[0052] Cloud computing is a model of service delivery 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 provider of the service. This cloud model can be composed of at least five characteristics, at least three service models, and at least four deployment models.

[0053] The features are as follows.

[0054] Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0055] Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0056] Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but can be able to specify location at a higher level of abstraction (e.g., country, state, or data center).

[0057] Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly release to quickly scale in. To the consumer, the provisioned capacity can generally appear to be unlimited and can be purchased in any quantity at any time.

[0058] Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the

[0059] The service models are as follows.

[0060] Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0061] Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. 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 possibly application hosting environment configurations.

[0062] Infrastructure as a Service (laaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include an operating system and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

[0063] Deployment models are as follows.

[0064] Private cloud: the cloud infrastructure is operated solely for an organization. It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0065] Community cloud: the cloud infrastructure is shared by several organizations and supports mission-oriented business

[0066] Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

[0067] Hybrid cloud: the cloud infrastructure is a composition of two or more types of cloud (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technologies that enable data and application portability.

[0068] Cloud computing environments are service-oriented, focusing on stateless, loosely coupled, modular, and semantically interoperable. The core of cloud computing is an infrastructure composed of a network of interconnected nodes.

[0069] Reference Figure 5, an illustrative cloud computing environment 500 is depicted. As shown, cloud computing environment 500 includes one or more cloud computing nodes 10 with which a cloud consumer can communicate via a network 20, for instance, the Internet, with the cloud computing nodes 10 being a server computer, a desktop computer, a laptop computer, notebook computer, netbook computer, a personal digital assistant, a cellular telephone, a Blackberry®, or other portable electronic device, a tablet computer, etc. Cloud computing nodes 10 can communicate with one another. They can be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 500 to offer infrastructure, platforms and / or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 11 are not exhaustive and that cloud computing nodes 10 and cloud computing environment 500 can also include other devices with which the cloud consumer can interact. Many of the examples provided herein are in the general context of web-based software applications and, thus, can also be implemented in the context of other software applications as well as hardware implementations. Figure 4

[0070] Referring now to FIG. 12, Figure 6 , a set of functional abstraction layers provided by cloud computing environment 500 (FIG. 11) is shown. It should be understood that the components, layers, and functions shown in FIG. 12 are intended to be illustrative only and that embodiments are not limited thereto. As depicted, the following layers and corresponding functions are provided: Figure 5 Figure 6

[0071] Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0072] 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.

[0073] ​​​In one example, management layer 80 can provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources can include application software licenses. Security provides identity verification 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 such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which future requirements are anticipated in accordance with an SLA.

[0074] Workloads layer 90 provides examples of functionality that can be utilized by the cloud computing environment. Examples of workloads and functions which can be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and video transcoding 96. Video transcoding 96 can encode and decode video data based on implicitly or explicitly selected hybrid transform kernels.

[0075] Some embodiments can relate to a system, a method, and / or a computer readable medium at any possible technical detail level of integration. The computer readable medium can include a computer-readable non-transitory storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out operations.

[0076] A computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is 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 the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted via a wire cable.

[0077] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0078] Computer readable program code / instructions for carrying out operations can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider (ISP)). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects or operations.

[0079] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including

[0080] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0081] The flow and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer-readable media according to various embodiments. In this regard, each block in the flow and block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). The methods, computer systems, and computer-readable media can include additional, fewer, or different blocks, and the blocks can be arranged in different orders. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in some cases, be executed concurrently or in the reverse order, depending on the functionality involved. Also, each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0082] It will be apparent that systems and / or methods described herein can be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and / or methods were described herein without reference to specific software code— it being understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0083] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and can be used interchangeably with “one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.), and can be used interchangeably with “one or more.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.

[0084] The description of the various aspects and embodiments has been presented for purposes of illustration, but is not intended to be exhaustive or limited to the embodiments disclosed. Although features of the combinations are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure to what can be claimed. Indeed, many modifications and variations to these features can be apparent to those of ordinary skill in the art. While each of the appended claims may stand on its own as a separate embodiment, each claim can also stand in combination with every other claim appearing anywhere in this application. Many modifications and variations of this disclosure can be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application, or technical improvement over technologies found in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method of decoding video data, the method comprising: comprises: receiving encoded video data; identifying a set of hybrid transform kernels corresponding to the encoded video data; selecting a subset of hybrid transform kernels from the set of hybrid transform kernels; and decoding the encoded video data based on the selected subset of hybrid transform kernels; wherein the set of hybrid transform kernels comprises a plurality of pairs of transform types, each pair of the plurality of pairs of transform types comprising a vertical transform type and a horizontal transform type; and for at least a portion of the plurality of pairs of transform types, at least one of the vertical transform type and the horizontal transform type of the pair is a line graph transform (LGT); wherein the plurality of pairs of transform types comprises a pair comprising a discrete cosine transform (DCT) vertical transform type and a DCT horizontal transform type, a pair comprising a line graph transform (LGT) vertical transform type and a LGT horizontal transform type, a pair comprising a DCT vertical transform type and a LGT horizontal transform type, and a pair comprising a LGT vertical transform type and a DCT horizontal transform type. The subset of hybrid transform kernels is implicitly selected.

2. The method of claim 1, wherein, The method further comprises selecting the subset of hybrid transform kernels based on at least one of an intra prediction mode and a block size associated with the received encoded video data.

3. The method of claim 2, wherein, The subset of hybrid transform kernels is selected based on one or more of a direct current mode (DC), a smooth mode (SMOOTH), a horizontal smooth mode (SMOOTH H), a vertical smooth mode (SMOOTH V), a vertical mode (V_PRED), a horizontal mode (H_PRED), chroma-from-luma, and Paeth.

4. The method of claim 3, wherein, The subset of hybrid transform kernels is explicitly selected.

5. The method of claim 1, wherein, The subset of hybrid transform kernels is identified by a syntax element signaled in a bitstream associated with the encoded video data.

6. The method of claim 5, wherein, The method further comprises applying an explicit transform scheme for all intra prediction modes.

7. The method of claim 5, wherein, The number of hybrid transform candidates is different for different intra prediction modes.

8. The method of claim 7, wherein, The method further comprises applying an explicit transform scheme for a subset of intra prediction modes.

9. The method of claim 5, wherein, The subset of hybrid transform kernels switches between explicit and implicit based on high-level syntax or signaling at block level.

10. The method according to any one of claims 1 to 9, characterized in that, The method comprises:

11. A method of encoding video data, the method comprising: determining a set of hybrid transform kernels corresponding to video data; selecting a subset of hybrid transform kernels from the set of hybrid transform kernels; and encoding the video data based on the selected subset of hybrid transform kernels to obtain encoded video data; transmitting the encoded video data; wherein the set of hybrid transform kernels comprises a plurality of pairs of transform types, each pair of the plurality of pairs of transform types comprising a vertical transform type and a horizontal transform type; and for at least a portion of the plurality of pairs of transform types, at least one of the vertical transform type and the horizontal transform type of the pair is a line graph transform (LGT); ​ Among the pairs of transform types are pairs including a discrete cosine transform (DCT) vertical transform type and a DCT horizontal transform type, pairs including a line graph transform (LGT) vertical transform type and a LGT horizontal transform type, pairs including a DCT vertical transform type and a LGT horizontal transform type, and pairs including a LGT vertical transform type and a DCT horizontal transform type.

12. A computer system for decoding video data, the computer system comprising: Comprising: a receiving module configured to receive encoded video data; an identifying module configured to identify a set of hybrid transform kernels corresponding to the encoded video data; a selecting module configured to select a subset of hybrid transform kernels from the set of hybrid transform kernels; and a decoding module configured to decode the encoded video data based on the selected subset of hybrid transform kernels. Among the pairs of transform types are pairs including a discrete cosine transform (DCT) vertical transform type and a DCT horizontal transform type, pairs including a line graph transform (LGT) vertical transform type and a LGT horizontal transform type, pairs including a DCT vertical transform type and a LGT horizontal transform type, and pairs including a LGT vertical transform type and a DCT horizontal transform type. The subset of hybrid transform kernels is implicitly selected. The selecting module is further configured to select the subset of hybrid transform kernels based on at least one of an intra prediction mode and a block size associated with the received encoded video data.

13. The computer system of claim 12, wherein, The subset of hybrid transform kernels is selected based on one or more of a direct current mode DC, a smooth mode SMOOTH, a horizontal smooth mode SMOOTH H, a vertical smooth mode SMOOTH V, a vertical mode V PRED, a horizontal mode H PRED, chroma-from-luma, and Paeth.

14. The computer system of claim 13, wherein, The subset of hybrid transform kernels is explicitly selected.

15. The computer system of claim 14, wherein, The subset of hybrid transform kernels is identified by a syntax element signaled in a bitstream associated with the encoded video data.

16. The computer system of claim 12, wherein, The system further comprises an applying module configured to apply an explicit transform scheme for all intra prediction modes.

17. The computer system of claim 16, wherein, The number of hybrid transform candidates is different for different intra prediction modes.

18. The computer system of claim 16, wherein, The subset of hybrid transform kernels switches between explicit and implicit based on high level syntax or signaling at block level.

19. The computer system of claim 18, wherein, Comprising:

20. The computer system of any one of claims 12 to 19, wherein, one or more computer-readable non-transitory storage media storing computer program code; 21. A computer system for decoding video data, the computer system comprising: and one or more computer processors configured to access the computer program code and to carry out the method of any of claims 1-10 as instructed by the computer program code. The non-transitory computer-readable medium stores a computer program for decoding video data, wherein the computer program is configured to cause one or more computer processors to perform the method of any of claims 1-10. The non-transitory computer-readable medium stores a computer program for decoding video data, wherein the computer program is configured to cause one or more computer processors to perform the method of any of claims 1-10.

22. A non-transitory computer readable medium, comprising: ​ 23. A computer readable storage medium having stored thereon computer programs / instructions and a video bitstream, characterized in that, The computer program / instructions, when executed by the processor, implement the steps of the method of claim 11 to generate the video bitstream.

Images