Classifications

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  • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
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  • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
  • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
  • H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
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  • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
  • H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
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  • H04N19/593—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
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  • H04N19/82—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation involving filtering within a prediction loop
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  • H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards

Definitions

• This disclosure relates generally to field of data processing, and more particularly to video encoding and decoding.
• AOMedia Video 1 is an open video coding format designed for video transmissions over the Internet. It was developed as a successor to VP9 by the Alliance for Open Media (AOMedia), a consortium founded in 2015 that includes semiconductor firms, video on demand providers, video content producers, software development companies and web browser vendors. Many of the components of the AVI project were sourced from previous research efforts by Alliance members. Individual contributors started experimental technology platforms years before: Xiph's/Mozilla's Daala already published code in 2010, Google's experimental VP9 evolution project VPIO was announced on 12 September 2014, and Cisco's Thor was published on 11 August 2015. Building on the codebase of VP9, AVI incorporates additional techniques, several of which were developed in these experimental formats.
• the first version 0.1.0 of the AVI reference codec was published on 7 April 2016.
• a validated version 1.0.0 of the specification was released.
• a validated version 1.0.0 with Errata 1 of the specification was released.
• the AVI bitstream specification includes a reference video codec.
• Embodiments relate to a method, system, and computer readable medium for coding video data.
• a method for coding video data may include receiving video data.
• a directionality of a sample block of the received video data is determined.
• the directionality corresponds to a descriptor, such as edge direction or gradient.
• a geometric transformation is applied based on the determined directionality.
• the video data is decoded based on the applied geometric transformation.
• a computer system for coding 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 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, whereby the computer system is capable of performing a method.
• the method may include receiving video data. A directionality of a sample block of the received video data is determined. The directionality corresponds to a descriptor, such as edge direction or gradient. A geometric transformation is applied based on the determined directionality. The video data is decoded based on the applied geometric transformation.
• a computer readable medium for coding video data may 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 are executable by a processor for performing a method that may accordingly include receiving video data.
• a directionality of a sample block of the received video data is determined.
• the directionality corresponds to a descriptor, such as edge direction or gradient.
• a geometric transformation is applied based on the determined directionality.
• the video data is decoded based on the applied geometric transformation.
• FIG. 1 illustrates a networked computer environment according to at least one embodiment
• FIG. 2 is a diagram of exemplary edge directions of video data, according to at least one embodiment
• FIGS. 3A and 3B are exemplary geometric transformation processes, according to at least one embodiment
• FIG. 4 is a diagram of exemplary subsampling positions for samples corresponding to one or more gradient directions, according to at least one embodiment
• FIG. 5 is an operational flowchart illustrating the steps carried out by a program that codes video data based on applying a geometric transformation corresponding to an identified edge of the video data, according to at least one embodiment
• FIG. 6 is a block diagram of an illustrative cloud computing environment including the computer system depicted in FIG. 1, according to at least one embodiment
• FIG. 7 is a block diagram of functional layers of the illustrative cloud computing environment of FIG. 6, according to at least one embodiment.
• FIG. 8 is a block diagram of internal and external components of computers and servers depicted in FIG. 1 according to at least one embodiment.
• Embodiments relate generally to the field of data processing, and more particularly to video encoding and decoding.
• the following described exemplary embodiments provide a system, method and computer program to, among other things, encode and decode video data based on applying a geometric transformation to a detected edge within the video data.
• some embodiments have the capacity to improve the field of computing by allowing for improved encoding and decoding of video data through improved detecting of edges in the video data.
• AOMedia Video 1 is an open video coding format designed for video transmissions over the Internet. It was developed as a successor to VP9 by the Alliance for Open Media (AOMedia), a consortium founded in 2015 that includes semiconductor firms, video on demand providers, video content producers, software development companies and web browser vendors. Many of the components of the AVI project were sourced from previous research efforts by Alliance members. Individual contributors started experimental technology platforms years before: Xiph's/Mozilla's Daala already published code in 2010, Google's experimental VP9 evolution project VPIO was announced on 12 September 2014, and Cisco's Thor was published on 11 August 2015. Building on the codebase of VP9, AVI incorporates additional techniques, several of which were developed in these experimental formats.
• the first version 0.1.0 of the AVI reference codec was published on 7 April 2016.
• a validated version 1.0.0 of the specification was released.
• a validated version 1.0.0 with Errata 1 of the specification was released.
• the AVI bitstream specification includes a reference video codec.
• cross-component filtering method is defined as a filtering process which uses the reconstructed samples of a first color component as input (e.g., Y or Cb or Cr), and the output is applied on a second color component which is a different color component of the first color component.
• a first color component e.g., Y or Cb or Cr
• CCF Cross-Component filtering
• ALF geometric transformations of filter coefficients are applied to handle different directionality.
• the geometric transformation of filter coefficients can be also applied, and it may help improve the coding efficiency. It may be advantageous, therefore, to consider the directionality of samples when training filter coefficients and performing filtering, determine the directionality of samples, and apply a geometric transformation to either filter coefficients or samples located in the filter support region for improved coding efficiency.
• FIG. 1 a functional block diagram of a networked computer environment illustrating a video coding system 100 (hereinafter “system”) for encoding and decoding video data based on applying a geometric transformation corresponding to an identified edge of the video data.
• system video coding system 100
• FIG. 1 provides only 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 environments may be made based on design and implementation requirements.
• the system 100 may include a computer 102 and a server computer 114.
• the computer 102 may communicate with the server computer 114 via a communication network 110 (hereinafter “network”).
• the computer 102 may include a processor 104 and a software program 108 that is stored on a data storage device 106 and is enabled to interface with a user and communicate with the server computer 114.
• the computer 102 may include internal components 800 A and external components 900 A, respectively
• the server computer 114 may include internal components 800B and external components 900B, respectively.
• the computer 102 may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing devices capable of running a program, accessing a network, and accessing a database.
• 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 (laaS), as discussed below with respect to FIGS. 6 and 7.
• SaaS Software as a Service
• PaaS Platform as a Service
• laaS Infrastructure as a Service
• the server computer 114 may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.
• the server computer 114 which may be used for coding video data based on applying a geometric transformation corresponding to an identified edge of the video data is enabled to run a Video Coding Program 116 (hereinafter “program”) that may interact with a database 112.
• the Video Coding Program method is explained in more detail below with respect to FIG. 5.
• the computer 102 may operate as an input device including a user interface while the program 116 may run primarily on server computer 114.
• the program 116 may run primarily on one or more computers 102 while the server computer 114 may be used for processing and storage of data used by the program 116.
• the program 116 may be a standalone program or may be integrated into a larger video coding program.
• processing for the program 116 may, in some instances be shared amongst the computers 102 and the server computers 114 in any ratio.
• the program 116 may operate on more than one computer, server computer, or some combination of computers and server computers, for example, a plurality of computers 102 communicating across the network 110 with a single server computer 114.
• the program 116 may operate on a plurality of server computers 114 communicating across the network 110 with a plurality of client computers.
• the program may operate on a network server communicating across the network with a server and a plurality of client computers.
• the network 110 may include wired connections, wireless connections, fiber optic connections, or some combination thereof.
• the network 110 can be any combination of connections and protocols that will support communications between the computer 102 and the server computer 114.
• the network 110 may include various types of networks, such as, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, a telecommunication network such as the 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 optic-based network, or the like, and/or a combination of these or other types of networks.
• LAN local area network
• WAN wide
• FIG. 1 The number and arrangement of devices and networks shown in FIG. 1 are provided as an example. In practice, there may be 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. 1. Furthermore, two or more devices shown in FIG. 1 may be implemented within a single device, or a single device shown in FIG. 1 may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of system 100 may perform one or more functions described as being performed by another set of devices of system 100.
• a set of devices e.g., one or more devices
• An edge may denote a set of samples located at continuous positions of the video and image data where an abrupt change of intensity values occurs.
• An edge may have a direction that may denote a unit vector along the edge.
• An edge may have a position that may correspond to a sample position at which the edge is located.
• Edge detection therefore, may correspond to a process that may be used to determine the edge direction and edge position. For example, edge detection may be performed by determining a gradient along the edge. The gradient may correspond to an increase or decrease of sample values along a direction which may be different from edge direction.
• a negative gradient value may correspond to sample intensity changing to smaller ones along a given direction, while a positive gradient value may correspond to sample intensity changing to larger ones along a given direction.
• the edge directions 200 may include, for example, a horizontal edge 202, a vertical edge 204, a 45-degree edge 206, and a 135-degree edge 208. It may be appreciated that given a large enough sample block size, substantially any angle of edge may exist.
• a directionality of an M x N block may be determined.
• Example values of M and N may include, for example, 2, 4, and 8.
• An input of this process may be an M x N block of samples, and an output may be a descriptor that may describe the directionality of current block.
• edge direction is selected as t-e descriptor.
• An edge detection process may be applied to the current block, and the output of this process is edge direction and edge position.
• edge direction is determined based on a direction search of a block having a smallest error value. The direction search operates on the reconstructed pixels, just after the deblocking filter.
• a perfectly directional block is a block where all of the pixels along a line in one direction have the same value.
• edge detection is performed using Canny Edge detector. In one embodiment, edge detection is performed using Sobel Edge detector.
• gradient value is selected as descriptor.
• a process which computes the gradient value along specific directions is firstly applied to the current block.
• Example gradient directions include but not limited to horizontal, vertical, and diagonal (45- degree, 135-degree).
• the M x N block of the input color component of CCF is used to determine the directionality in the above proposed method.
• the M x N block is taken from Y component.
• the MxN block is taken from Cb component.
• the M x N block is taken from Cr component.
• the M x N block of the output color component of CCF is used to determine the directionality in the above proposed method.
• the M x N block is taken from Y component.
• the M x N block is taken from Cb component.
• the M x N block is taken from Cr component.
• the geometric transformation process 300A may be applied in the filtering process of CCF.
• the input to this process is the directionality of samples in the examples described in reference to embodiments relevant to FIG. 2 above.
• the filter coefficients of CCF or the reconstructed samples in the filter support region are transformed.
• the filter coefficients or reconstructed samples in the filter support region are rotated based on the identified edge direction.
• the target direction only includes one single direction.
• the target direction is horizontal direction. The rotation scheme is illustrated. When the edge direction of current block is horizontal, no rotation is performed.
• the filter coefficients or the samples in the filter support region are rotated 90 degree, clockwise.
• the filter coefficients or the samples in the filter support region are rotated 135 degree, clockwise.
• the filter coefficients or the samples in the filter support region are rotated 45 degree, clockwise.
• current block is a smooth area, i.e., no edge detected, no rotation is performed.
• the geometric transformation process may include, among other things, one or more pre-defmed rotations 304A-B.
• the geometric transformation process 300B may include applying the same transformation to multiple directions.
• the target directions are horizontal and vertical.
• the rotation scheme is illustrated. When the edge direction of current block is horizontal, no rotation is performed. When the edge direction of current block is vertical, no rotation is performed. When the edge direction of current block is 45-degree, the filter coefficients or the samples in the filter support region are rotated 45 degree, clockwise. When the edge direction of current block is 135-degree, the filter coefficients or the samples in the filter support region are rotated 45 degree, clockwise.
• rotation is performed when CCF scans all samples within current block, regardless of whether the sample is located on the edge.
• saying CCF scans a sample it refers to either one of the following two cases: 1) the sample undergoes training, or 2) filtering process of CCF.
• rotation is performed only when CCF scans the samples that are located on the edge within current block.
• saying CCF scans a sample it means the sample undergoes training or filtering of CCF.
• the filter coefficients or reconstructed samples in the filter support region are transformed based on the gradient value.
• geometric transformations of filter coefficients and clipping values may be used to perform the transformation.
• An Adaptive Loop Filter (ALF) with block- based filter adaption may be applied.
• ALF Adaptive Loop Filter
• For the luma component one among 25 filters is selected for each 4x4 block, based on the direction and activity of local gradients.
• Two diamond filter shapes may be used for adaptive-loop filtering.
• a 7x7 diamond shape may be applied for the luma component and a 5x5 diamond shape may be applied for the chroma component.
• each 4 x 4 block may be categorized into one out of 25 classes.
• indices i and j refer to the coordinates of the upper left sample within the 4 x 4 block and R(i, j) indicates a reconstructed sample at coordinate (i, j).
• the subsampled 1-D Laplacian calculation is applied. As will be discussed below with regard to FIG. 4, the same subsampled positions are used for gradient calculation of all directions. Then D maximum and minimum values of the gradients of horizontal and vertical directions may be set as:
• the activity value A may be calculated as: where A may be further quantized to the range of 0 to 4, inclusively, and the quantized value is denoted as ⁇ .
• A may be further quantized to the range of 0 to 4, inclusively, and the quantized value is denoted as ⁇ .
• no classification method may need be applied, i.e. a single set of ALF coefficients may be applied for each chroma component.
• K is the size of the filter and 0 ⁇ k, 1 ⁇ K — 1 are coefficients coordinates, such that location (0,0) is at the upper left comer and location (K — 1, K — 1) is at the lower right comer.
• the transformations are applied to the filter coefficients f (k, 1) and to the clipping values c(k, 1) depending on gradient values calculated for that block. For gradient values gd2 ⁇ gdl and gh ⁇ gv, there may be no transformation that need be applied.
• a diagonal transformation may be applied.
• a vertical flip transformation may be applied.
• a rotation transformation may be applied.
• the directionality of the block is measured by both edge direction and gradient, such that the transformation of filter coefficients of CCF depends on both edge direction and gradient.
• the edge direction determines whether the filter coefficients are rotated.
• the gradient determines whether the filter coefficients are flipped.
• geometric transformation is applied in the training process of CCF.
• the input to this process is the directionality of samples in the examples described in reference to embodiments relevant to FIG. 2 above .
• the filter coefficients of CCF or the reconstructed samples in the filter support region are transformed.
• the subsampling positions may include positions for a sample 302 A corresponding to a vertical gradient, a sample 302B corresponding to a horizontal gradient, a sample 302C corresponding to a first diagonal gradient, and a sample 302D corresponding to a second diagonal gradient.
• the subsampling positions V, H, Dl, and D2 may correspond to the vertical, horizontal, first diagonal, and second diagonal gradients, respectively.
• FIG. 5 an operational flowchart illustrating the steps of a method 500 for coding video data is depicted.
• one or more process blocks of FIG. 5 may be performed by the computer 102 (FIG. 1) and the server computer 114 (FIG. 1).
• one or more process blocks of FIG. 5 may be performed by another device or a group of devices separate from or including the computer 102 and the server computer 114.
• the method 500 includes receiving video data.
• the method 500 includes determining a directionality of a sample block of the received video data, the directionality corresponding to a descriptor.
• the method 500 includes applying a geometric transformation based on the determined directionality.
• the method 500 includes decoding the video data based on the applied geometric transformation.
• FIG. 5 provides only an illustration of one implementation and does not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.
• 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 may include at least five characteristics, at least three service models, and at least four deployment models.
• On-demand self-service a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service’s provider.
• 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 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 may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
• Rapid elasticity capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
• 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 utilized service.
• level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts).
• SaaS Software as a Service: 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).
• 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.
• PaaS Platform as a Service
• 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.
• laaS Infrastructure as a Service
• 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).
• Private cloud the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
• 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.
• Hybrid cloud the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds).
• a cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability.
• An infrastructure comprising a network of interconnected nodes.
• cloud computing environment 600 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate.
• Cloud computing nodes 10 may communicate with one another. They may 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 600 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.
• computing devices 54A-N shown in FIG. 6 are intended to be illustrative only and that cloud computing nodes 10 and cloud computing environment 600 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
• FIG. 7 a set of functional abstraction layers 700 provided by cloud computing environment 600 (FIG. 6) is shown. It should be understood in advance that the components, layers, and functions shown in FIG. 7 are intended to be illustrative only and embodiments are not limited thereto. As depicted, the following layers and corresponding functions are provided:
• Hardware and software layer 60 includes hardware and software components.
• 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.
• software components include network application server software 67 and database software 68.
• Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may 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.
• management layer 80 may 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 may comprise 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 a future requirement is anticipated in accordance with an SLA.
• SLA Service Level Agreement
• Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may 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 Coding 96. Video Coding 96 may apply a geometric transformation corresponding to an identified edge of the video data.
• FIG. 8 is a block diagram of internal and external components of computers 102, 114 depicted in FIG. 1 in accordance with an illustrative embodiment. It should be appreciated that FIG. 8 provides only 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 environments may be made based on design and implementation requirements.
• Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may include respective sets of internal components 800A,B and external components 900A,B illustrated in FIG 4.
• Each of the sets of internal components 800 include one or more processors 820, one or more computer- readable RAMs 822 and one or more computer-readable ROMs 824 on one or more buses 826, one or more operating systems 828, and one or more computer-readable tangible storage devices 830.
• 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.
• processor 820 includes one or more processors capable of being programmed to perform a function.
• Bus 826 includes a component that permits communication among the internal components 800 A, B.
• the one or more operating systems 828, the software program 108 (FIG. 1) and the Video Coding Program 116 (FIG. 1) on server computer 114 (FIG. 1) are stored on one or more of the respective computer-readable tangible storage devices 830 for execution by one or more of the respective processors 820 via one or more of the respective RAMs 822 (which typically include cache memory).
• each of the computer-readable tangible storage devices 830 is a magnetic disk storage device of an internal hard drive.
• each of the computer-readable tangible storage devices 830 is a semiconductor storage device such as ROM 824, EPROM, flash memory, an optical disk, a magneto-optic disk, a solid state disk, a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable tangible storage device that can store a computer program and digital information.
• Each set of internal components 800A,B also includes a 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.
• a software program such as the software program 108 (FIG. 1) and the Video Coding Program 116 (FIG. 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.
• Each set of internal components 800A,B also includes network adapters or interfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interface cards; or 3G, 4G, or 5G wireless interface cards or other wired or wireless communication links.
• the software program 108 (FIG. 1) and the Video Coding Program 116 (FIG. 1) on the server computer 114 (FIG. 1) can be downloaded to the computer 102 (FIG. 1) and server computer 114 from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces 836.
• the network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
• Each of the sets of external components 900A,B can include a computer display monitor 920, a keyboard 930, and a computer mouse 934. External components 900A,B can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices.
• Each of the sets of internal components 800A,B also includes device drivers 840 to interface to computer display monitor 920, keyboard 930 and computer mouse 934.
• the 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).
• Some embodiments may relate to a system, a method, and/or a computer readable medium at any possible technical detail level of integration
• the computer readable medium may include a computer-readable non-transitory storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out operations.
• the 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 may 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.
• RAM random access memory
• ROM read-only memory
• EPROM or Flash memory erasable programmable read-only memory
• SRAM static random access memory
• CD-ROM compact disc read-only memory
• DVD digital versatile disk
• memory stick a floppy disk
• a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon
• a computer readable storage medium 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 through a wire.
• 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 may 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.
• Computer readable program code/instructions for carrying out operations may 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 procedural programming languages, such as the "C" programming language or similar programming languages.
• the computer readable program instructions may 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.
• the remote computer may 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 may be made to an external computer (for example, through the Internet using an Internet Service Provider).
• electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may 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.
• These computer readable program instructions may 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 may 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 instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
• the computer readable program instructions may 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.
• each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
• the method, computer system, and computer readable medium may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in the Figures.
• the functions noted in the blocks may occur out of the order noted in the Figures.
• 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.

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