Nonessential input, output and task signaling in workflows on cloud platforms

Systems and methods for managing a Network Based Media Processing (NBMP) workflow are provided. A method includes a method performed by at least one processor is provided. The method includes: deriving the NBMP workflow; obtaining at least one first syntax element indicating that at least one task included in the NBMP workflow, at least one input received by the at least one task, or at least one output generated by the at least one task, is nonessential; determining a plurality of essential tasks based on the at least one first syntax element; and assigning the plurality of essential tasks to at least one from among a media sink, a media source, and a media processing entity included in the NBMP workflow.

FIELD

Embodiments of the present disclosure are directed to Moving Picture Experts Group (MPEG) Network Based Media Processing (NBMP) and, more particularly, to managing an NBMP workflow.

BACKGROUND

MPEG Network Based Media Processing (NBMP) project has developed a concept of processing media on cloud. The NBMP Draft International Specification shows a great potential to increase media processing efficiency, faster and lower-cost deployment of media services, and the ability to provide large scale deployment by leveraging the public, private or hybrid cloud services.

However, the current NBMP specification does not distinguish between the tasks that are essential for the workflow and the ones that are not essential.

Further, while the division of media streams with equal duration segments is useful for many applications, in some applications the duration of segments may vary due to different reasons. Therefore fixed duration segments would not work in these cases.

In addition, the NBMP standard defines a step descriptor for processing media in temporal segments independent from each other, or in parallel. While the division of media streams in time is the first level of parallelism, for large video or volumetric data, segmentation in other dimensions can further provide more parallelism or independent processing of media data and enabling using more parallel tasks/microservices for processing the data. Such multidimensional parallelism is insufficiently addressed in the NBMP standard.

In addition, the NBMP standard defines Workflow Description to define the processing required. However, the exclusion of some of the function instances is not possible with the current design. While NBMP Workflow Description provides a detailed requirement for running a workflow, it does not allow the exclusion of some function instances from the descriptions that are applied to the entire workflow.

SUMMARY

The NBMP Draft International Specification shows a great potential to increase media processing efficiency, to provide faster and lower-cost deployment of media services, and to have the ability to provide large scale deployment by leveraging public, private, or hybrid cloud services.

Embodiments of the present disclosure provide a mechanism to implement various changes in the NBMP standard.

In embodiments, there is provided a signaling mechanism to identify nonessential inputs, outputs, and tasks, and to derive the essentiality of inputs and outputs from the essentiality of tasks.

In embodiments, there is provided a method for independent processing of media segments which may have different durations or sizes in the cloud, which may involve extending the NBMP standard to support independent processing of segments with variable duration/dimensions.

In embodiments, there is provided an extension of the NBMP standard's step descriptor to define multi-dimensional segments.

In embodiments, there is provided a method for exclusion of some function instances from the descriptions that are applied to the entire workflow.

According to one or more embodiments, a method performed by at least one processor is provided. The method includes: deriving a network based media processing (NBMP) workflow; obtaining at least one first syntax element indicating that at least one task included in the NBMP workflow, at least one input received by the at least one task, or at least one output generated by the at least one task, is nonessential; determining a plurality of essential tasks based on the at least one first syntax element; and assigning the plurality of essential tasks to at least one from among a media sink, a media source, and a media processing entity included in the NBMP workflow.

According to one or more embodiments, a workflow manager of a media system is provided. The workflow manager includes: at least one processor; and memory including computer code. The computer code includes: workflow deriving code configured to cause the at least one processor to derive a network based media processing (NBMP) workflow; first obtaining code configured to cause the at least one processor to obtain at least one first syntax element indicating that at least one task included in the NBMP workflow, at least one input received by the at least one task, or at least one output generated by the at least one task, is nonessential; determining code configured to cause the at least one processor to determine a plurality of essential tasks based on the at least one first syntax element; and assigning code configured to cause the at least one processor to assign the plurality of essential tasks to at least one from among a media sink, a media source, and a media processing entity included in the NBMP workflow.

According to one or more embodiments, a non-transitory computer-readable medium storing computer code is provided. The computer code is configured to, when executed by at least one processor that implements a workflow manager of a media system, cause the at least one processor to: derive a network based media processing (NBMP) workflow; obtain at least one first syntax element indicating that at least one task included in the NBMP workflow, at least one input received by the at least one task, or at least one output generated by the at least one task, is nonessential; determine a plurality of essential tasks based on the at least one first syntax element; and assign the plurality of essential tasks to at least one from among a media sink, a media source, and a media processing entity included in the NBMP workflow.

DETAILED DESCRIPTION

FIG.1is a diagram of an environment100in which methods, apparatuses, and systems described herein may be implemented, according to embodiments. As shown inFIG.1, the environment100may include a user device110, a platform120, and a network130. Devices of the environment100may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections.

The user device110includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with platform120. For example, the user device110may include a computing device (e.g. a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g. a smart phone, a radiotelephone, etc.), a wearable device (e.g. a pair of smart glasses or a smart watch), or a similar device. In some implementations, the user device110may receive information from and/or transmit information to the platform120.

The platform120includes one or more devices as described elsewhere herein. In some implementations, the platform120may include a cloud server or a group of cloud servers. In some implementations, the platform120may be designed to be modular such that software components may be swapped in or out depending on a particular need. As such, the platform120may be easily and/or quickly reconfigured for different uses.

In some implementations, as shown, the platform120may be hosted in a cloud computing environment122. Notably, while implementations described herein describe the platform120as being hosted in the cloud computing environment122, in some implementations, the platform120may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

The cloud computing environment122includes an environment that hosts the platform120. The cloud computing environment122may provide computation, software, data access, storage, etc. services that do not require end-user (e.g. the user device110) knowledge of a physical location and configuration of system(s) and/or device(s) that hosts the platform120. As shown, the cloud computing environment122may include a group of computing resources124(referred to collectively as “computing resources124” and individually as “computing resource124”).

The computing resource124includes one or more personal computers, workstation computers, server devices, or other types of computation and/or communication devices. In some implementations, the computing resource124may host the platform120. The cloud resources may include compute instances executing in the computing resource124, storage devices provided in the computing resource124, data transfer devices provided by the computing resource124, etc. In some implementations, the computing resource124may communicate with other computing resources124via wired connections, wireless connections, or a combination of wired and wireless connections.

As further shown inFIG.1, the computing resource124includes a group of cloud resources, such as one or more applications (“APPs”)124-1, one or more virtual machines (“VMs”)124-2, virtualized storage (“VSs”)124-3, one or more hypervisors (“HYPs”)124-4, or the like.

The application124-1includes one or more software applications that may be provided to or accessed by the user device110and/or the platform120. The application124-1may eliminate a need to install and execute the software applications on the user device110. For example, the application124-1may include software associated with the platform120and/or any other software capable of being provided via the cloud computing environment122. In some implementations, one application124-1may send/receive information to/from one or more other applications124-1, via the virtual machine124-2.

The virtual machine124-2includes a software implementation of a machine (e.g. a computer) that executes programs like a physical machine. The virtual machine124-2may be either a system virtual machine or a process virtual machine, depending upon use and degree of correspondence to any real machine by the virtual machine124-2. A system virtual machine may provide a complete system platform that supports execution of a complete operating system (“OS”). A process virtual machine may execute a single program, and may support a single process. In some implementations, the virtual machine124-2may execute on behalf of a user (e.g. the user device110), and may manage infrastructure of the cloud computing environment122, such as data management, synchronization, or long-duration data transfers.

The hypervisor124-4may provide hardware virtualization techniques that allow multiple operating systems (e.g. “guest operating systems”) to execute concurrently on a host computer, such as the computing resource124. The hypervisor124-4may present a virtual operating platform to the guest operating systems, and may manage the execution of the guest operating systems. Multiple instances of a variety of operating systems may share virtualized hardware resources.

FIG.2is a block diagram of example components of one or more devices ofFIG.1. The device200may correspond to the user device110and/or the platform120. As shown inFIG.2, the device200may include a bus210, a processor220, a memory230, a storage component240, an input component250, an output component260, and a communication interface270.

The bus210includes a component that permits communication among the components of the device200. The processor220is implemented in hardware, firmware, or a combination of hardware and software. The processor220is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, the processor220includes one or more processors capable of being programmed to perform a function. The memory230includes a random access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device (e.g. a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by the processor220.

The input component250includes a component that permits the device200to receive information, such as via user input (e.g. a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and/or a microphone). Additionally, or alternatively, the input component250may include a sensor for sensing information (e.g. a global positioning system (GPS) component, an accelerometer, a gyroscope, and/or an actuator). The output component260includes a component that provides output information from the device200(e.g. a display, a speaker, and/or one or more light-emitting diodes (LEDs)).

The communication interface270includes a transceiver-like component (e.g. a transceiver and/or a separate receiver and transmitter) that enables the device200to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface270may permit the device200to receive information from another device and/or provide information to another device. For example, the communication interface270may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like.

The device200may perform one or more processes described herein. The device200may perform these processes in response to the processor220executing software instructions stored by a non-transitory computer-readable medium, such as the memory230and/or the storage component240. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spread across multiple physical storage devices.

Software instructions may be read into the memory230and/or the storage component240from another computer-readable medium or from another device via the communication interface270. When executed, software instructions stored in the memory230and/or the storage component240may cause the processor220to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

The number and arrangement of components shown inFIG.2are provided as an example. In practice, the device200may include additional components, fewer components, different components, or differently arranged components than those shown inFIG.2. Additionally, or alternatively, a set of components (e.g. one or more components) of the device200may perform one or more functions described as being performed by another set of components of the device200.

In an embodiment of the present disclosure, an NBMP system300is provided. With reference toFIG.3, the NBMP system300comprises an NBMP source310, an NBMP workflow manager320, a function repository330, one or more media processing entities350, a media source360, and a media sink370.

The NBMP source310may receive instructions from a third party entity, may communicate with the NBMP workflow manager320via an NBMP workflow API392, and may communicate with the function repository330via a function discovery API391. For example, the NBMP source310may send a workflow description document(s) (WDD) to the NBMP workflow manager320, and may read the function description of functions stored in the function repository330, the functions being media processing functions stored in memory of the function repository330such as, for example, functions of media decoding, feature point extraction, camera parameter extraction, projection method, seam information extraction, blending, post-processing, and encoding. The NBMP source310may comprise or be implemented by at least one processor and memory that stores code configured to cause the at least processor to perform the functions of the NBMP source310.

The NBMP source310may request the NBMP workflow manager320to create workflow including tasks352to be performed by the one or more media processing entities350by sending the workflow description document, which may include several descriptors, each of which may have several parameters.

For example, the NBMP source310may select functions stored in the function repository330and send the workflow description document to the NBMP workflow manager320that includes a variety of descriptors for description details such as input and output data, required functions, and requirements for the workflow. The workflow description document may include a set of task descriptions and a connection map of inputs and outputs of tasks352to be performed by one or more of the media processing entities350. When the NBMP workflow manager320receives such information from the NBMP source310, the NBMP workflow manager320may create the workflow by instantiating the tasks based on function names and connecting the tasks in accordance with the connection map.

Alternatively or additionally, the NBMP source310may request the NBMP workflow manager320to create workflow by using a set of keywords. For example, NBMP source310may send the NBMP workflow manager320the workflow description document that may include a set of keywords that the NBMP workflow manager320may use to find appropriate functions stored in the function repository330. When the NBMP workflow manager320receives such information from the NBMP source310, the NBMP workflow manager320may create the workflow by searching for appropriate functions using the keywords that may be specified in a Processing Descriptor of the workflow description document, and use the other descriptors in the workflow description document to provision tasks and connect them to create the workflow.

The NBMP workflow manager320may communicate with the function repository330via a function discovery API393, which may be a same or different API from the function discovery API391, and may communicate with one or more of the media processing entities350via an API394(e.g. an NBMP task API). The NBMP workflow manager320may comprise or be implemented by at least one processor and memory that stores code configured to cause the at least processor to perform the functions of the NBMP workflow manager320.

The NBMP workflow manager320may use the API394to setup, configure, manage, and monitor one or more tasks352of a workflow that is performable by the one or more media processing entities350. In an embodiment, the NBMP workflow manager320may use the API394to update and destroy the tasks352. In order to configure, manage, and monitor tasks352of the workflow, the NBMP workflow manager320may send messages, such as requests, to one or more of the media processing entities350, wherein each message may have several descriptors, each of which have several parameters. The tasks352may each include media processing functions354and configurations353for the media processing functions354.

In an embodiment, after receiving a workflow description document from the NBMP source310that does not include a list of the tasks (e.g. includes a list of keywords instead of a list of tasks), the NBMP workflow manager320may select the tasks based on the descriptions of the tasks in the workflow description document to search the function repository330, via the function discovery API393, to find the appropriate functions to run as tasks352for a current workflow. For example, the NBMP workflow manager320may select the tasks based on keywords provided in the workflow description document. After the appropriate functions are identified by using the keywords or the set of task descriptions that is provided by the NBMP source310, the NBMP workflow manager320may configure the selected tasks in the workflow by using the API394. For example, the NBMP workflow manager320may extract configuration data from information received from the NBMP source, and configure the tasks352based on the configuration data.

The one or more media processing entities350may be configured to receive media content from the media source360, process the media content in accordance with the workflow, that includes tasks352, created by the NBMP workflow manager320, and output the processed media content to the media sink370. The one or more media processing entities350may each comprise or be implemented by at least one processor and memory that stores code configured to cause the at least processor to perform the functions of the media processing entities350.

The media source360may include memory that stores media and may be integrated with or separate from the NBMP source310. In an embodiment, the NBMP workflow manager320may notify the NBMP source310when a workflow is prepared and the media source360may transmit media content to the one or more of the media processing entities350based on the notification that the workflow is prepared.

The media sink370may comprise or be implemented by at least one processor and at least one display that is configured to display the media that is processed by the one or more media processing entities350.

As discussed above, messages from the NBMP Source310(e.g. a workflow description document for requesting creation of a workflow) to the NBMP workflow manager320, and messages (e.g. for causing the workflow to be performed) from the NBMP workflow manager320to the one or more media processing entities350may include several descriptors, each of which may have several parameters. In cases, communication between any of the components of the NBMP system300using an API may include several descriptors, each of which may have several parameters.

[Nonessential Input, Output and Task Signaling in Workflows]

Embodiments may relate to a method to identify and signal the nonessential inputs, outputs and tasks in a workflow run on the cloud platforms.

An essential output of a workflow may be an output of that workflow that must produce data for the workflow to be considered as operating properly. An essential input of the workflow may be an input that must be processed for the workflow to create its essential outputs. A properly operating workflow may be a workflow that processes all of its essential inputs and produces all of its essential outputs. An essential task of a workflow may be a task necessary to operate properly and process data that is required for a properly operating workflow. For example, an essential task may be a task that processes an essential input, and/or produces an essential output. In embodiments, an essential input may be an input that is needed for an essential task to operate, and an essential output may be an output that is needed as an essential input for an essential task, or an output that is needed as an output for the workflow as a whole. A nonessential input may be an input that is not needed by the workflow in order to produce the essential outputs of the workflow. For example, a workflow may produce all of the essential outputs if all of the essential inputs are provided, even if none of the nonessential inputs are provided. A nonessential task may be a task that is included in the workflow, but that is not an essential task. For example, a nonessential task may process a nonessential input and produce a nonessential output.

FIG.4shows an example of a workflow400with essential and nonessential tasks, according to embodiments.

In workflow400, the nonessential tasks are shown with grey shaded circles. Therefore, as shown inFIG.4, Task1, Task3, Task5, and Task8are essential. Further, in the example shown inFIG.5, Task2, Task4, Task6, and Task7are nonessential.

Accordingly, in workflow400, Input1can be categorized as an essential input, while Input2can be categorized as a nonessential input. Similarly, Output2can be categorized as an essential output, while Output1and Output3can be categorized as nonessential outputs.

[Signalling Essentiality in a Workflow]

In embodiments, a flag may be used to signal the essentiality of a task in a given workflow. By defining all essential and nonessential tasks of a workflow, the essential and nonessential inputs and outputs may also be defined or determined.

Table 1 shows an example in which a nonessentiality flag is added to a Task's general descriptor. In Table 1, and throughout the present disclosure, italics are used to show additions.

TABLE 1Task's General Descriptor with added nonessentiality flagParameter NameTypeCardinalityidP1nameP1descriptionP1rankP0-1mpeg-compatibilityP0-1published-timeP0-1priorityP0-1execution-timeP0-1input-portsArray of object1output-portsArray of object1is-groupP0-1nonessentialP0-1stateP1

The added parameter is shown in italics. An example of a nonessential parameter is defined in Table 2.

If a workflow is defined by its graph of tasks, and if the tasks are identified as essential and nonessential, any input or output of a workflow can be identified to be essential or nonessential.

For a given workflow as a graph of essential and nonessential tasks, for any input and output pair, if the input can be connected to the output through at least one essential path, then both the input and output are essential. Any other input and output that can not satisfy the above condition are nonessential.

A path in a workflow may be a subset of that workflow that has only one input and one output, and the input may be connected to the output through a set of arrows connecting the tasks in the path, with the direction of each connection between two tasks being in the same direction of an arrow connecting input directly to the output. An essential path of a workflow may be a path in which all of the tasks included in the path are essential.

FIG.5illustrates one essential path in workflow400with a dashed arrow line.

[Signalling the Essentiality of Input and Outputs]

If a workflows graph is defined with the defined essentiality of its tasks, as shown above, the essentiality of the workflow's inputs and outputs can be derived.

However, in some cases, in a workflow description, only its inputs and outputs may be defined, while the exact workflow is left for a workflow manager to be derived. In this case, the workflow description may include or indicate the essentiality of its inputs and outputs.

In embodiments, a nonessentiality parameter may be defined for each output in the output descriptors, as shown in Table 3, Table 4, Table 5 and Table 6. For example, the nonessentiality parameter may indicate whether an input, output, task, or other object is essential or nonessential.

In embodiments, an output may be nonessential if both media and metadata parameters are nonessential.

[Deriving the Essentiality and Nonessentiality of Tasks from a Workflow's Inputs and Outputs]

If a workflow inputs and outputs essentialities are defined, the essentiality of the tasks (and its inputs) of the workflow can be derived.

For a given workflow as a graph of tasks and identified essential outputs, the following process can be used to identify the essential tasks:First, for each nonessential input, if all other corresponding task's input are also nonessential, the task's outputs may be marked nonessential and the task may be removed. This process may be applied to all new nonessential inputs until no nonessential input is left for this process to be applied.Second, for each nonessential output, if all other corresponding task's outputs are also nonessential, the task's inputs may be marked nonessential and the task may be removed. This process may be applied until no nonessential output is left for this process to be applied.

FIG.6shows an example of such process. For example,FIG.6shows a workflow600A in which only the essentiality of the inputs are known. As can be seen inFIG.6A, Input1is known to be essential, Input2is known to be nonessential, Output2is known to be essential, and Output2and Output3are known to be nonessential.

FIG.6also shows workflow600B, which is a result of Operation610being performed on workflow600A. Operation610may correspond to the first portion of the process described above. For example, because Input2is nonessential, and because Task2has no other inputs, then the output of Task2(shown as Input2.1) is also be marked as nonessential, and Task2is removed.

FIG.6also shows workflow600C, which is a result of Operation620being performed on workflow600B. Operation620may correspond to the second portion of the process described above. For example, because Output1is nonessential, and because Task6has no other outputs, then the input of Task6(shown as Output1.2) is marked as nonessential, and Task6is removed. Similarly, because Task4has no essential outputs, then the input of Task6(shown as Output1.1) is also marked as nonessential, and Task6is removed. Further, because Output3is nonessential, and because Task7has no other outputs, then the input of Task7(shown as Output3) is also marked as nonessential, and Task7is removed.

Therefore, as a result of operation610and620, Task2, Task4, Task6and Task7are removed and therefore these tasks may be determined as nonessential tasks. Similarly, because Task1, Task3, Task5and Task8remain, these tasks may be determined as essential tasks.

Embodiments may relate to a method for defining essentiality of inputs, outputs, and tasks in a workflow, wherein an input, output, or task can be defined nonessential for the rest of workflow and if that entity is removed, the workflow is still properly operational.

Embodiments may relate to a method for signaling the essentiality of tasks, wherein a flag is used for signaling the nonessentiality of the task in its description.

Embodiments may relate to a method for signalling the essentiality of one input or one output, wherein a flag is used for signalling the nonessentiality of the input or the output in its description.

Embodiments may relate to a method of deriving the essentiality of inputs and outputs of a workflow when the essentiality of the workflow's tasks are identified wherein the essential paths of the workflow and the corresponding essential input-output pairs are identified.

Embodiments may relate to a method of deriving the essentiality of tasks of a workflow when the essentiality of the workflow's inputs and/or outputs are identified wherein using this method the workflow graph is simplified until all nonessential tasks are identified.

[Independent Processing of Media Segments with Variable Durations on Cloud]

Embodiments may relate to a method for independent processing of media segments which may have different durations or sizes in the cloud.

FIG.7shows an example of a process700of using split/merge for parallel processing of the segments. InFIG.7, Task T is converted to n instances of Task T (i.e., Task T0, Task T1, . . . , Task TN-1), running in parallel.

InFIG.7, the media stream is continuous. However, 1:N Split function converts the media stream to N media sub-streams. Each sub-stream is processed by an instance of T and then the sub-streams are interleaved together to generate the output, equivalent of Task T output stream.

1:N Split and N:1 Merge functions work on the segment boundaries. Each segment has a duration of equal to one or multiple T's Task Segment Duration (TSD), and therefore, the segments and consequently the sub-streams are independent of each other in terms of being processed by Task T. Note that Task T0, . . . , TN-1, do not need to process the segments at the same time. Since the segments and substreams are independent, each instance of Task can run at its speed.

Since each segment in each substream carries its start time, duration, and length, they can be multiplexed together in the right order.

The NBMP amendment defines the splitter and merger function template for the above operations.

The NBMP standard includes a Step Descriptor that describes processing mode and temporal segment duration for each function as shown in Table 7.

TABLE 7NBMP Step DescriptorValidNameDefinitionUnitTyperangestep-moderunning mode with the following values:N/AstringN/A‘stream’: continuous execution‘stateful’: maintain the state of tasks atend each step‘stateless’: run in stateless modewithout the need for maintaining stateThe default value is ‘stream’.segment-durationduration for which the output(s) ofmicro-numberunsignedresource are independent to any inputssecondsintegeroutside of the corresponding duration.operation-unitsnumber of segment-duration the resource isN/Anumberunsignedoperating in a stateless fashioninteger

As is shown in Table 7, the segment duration is the same for all segments.

[Extending the Step Descriptor]

In practice, the segment durations may vary segment to segment. Therefore, instead of the segment duration as the parameter of operation, embodiments may include a segment marker for the separation of the segment. In this case, it is useful to have a maximum segment duration. Embodiments may also signal whether a variable segment duration is allowed or not.

TABLE 8Step Descriptor with a variable segment durationValidNameDefinitionUnitTyperangestep-moderunning mode with the following values:N/AstringN/A‘stream’: continuous execution‘stateful’: maintain the state of tasks atend each step‘stateless’: run in stateless modewithout the need for maintaining stateThe default value is ‘stream’.segment-durationduration for which the output(s) ofmicro-numberunsignedresource are independent to any inputssecondsintegeroutside of the corresponding duration.This value indicates the maximumsegment-duration if the variable-durationis ‘True’.variable-durationIf set to ‘True’, the duration of segmentsN/AN/Abinarymay vary.The default is ‘False’.
[Extending the Splitter and Merger Reference Function Templates]

To convert a media stream with variable segment durations to multiple streams using the splitter function and then combined them after processing using the merger function, embodiments may extend the NBMP Splitter and Merger reference function templates in the following.

DESCRIPTION

These requirements assume that the input media stream is continuous without any gaps. In this clause, [a,b[ means a time range equal or greater than a and smaller than b.

The Splitter Function may have the following requirements:one input and N output FIFO buffers, where N is a configuration parameter for the number of splits.Operates one input segment at each time and generates N output segmentsEach input segments shall satisfy the following requirements:continuous set of samplesmaximum duration of N*D in scale of time-scale T, where D and T are configuration parametersinclude the following metadata and constraint:Start time s (msec)time-scale tmaximum duration N*d, where d=D*T/t is an integer number i.e. D*T is divisible by tlength l (bytes)segment marker when variable-duration=‘True’, where segment markers indicated the starting time of each segmentNon-overlapping samples with other input segmentsThe set of input segments shall cover the entire duration of input, i.e. no samples of input is left out from the total durationOperates on input segments in incrementing order, i.e a segment covering the earlier duration of the time shall be processed before any segment covering later duration.The media streams at every output buffer at any time consist of zero or more output segments. Each output segment shall satisfy the following requirementscontinuous duration of samplesmaximum duration of d in scale of time-scale tinclude the following metadata:Start time s (msec)Time-scale tmaximum duration d in scale of time-scale tlength l (bytes)The collection of all output segments of all output buffer together shall cover the entire duration of input, i.e. no sample of input is left out of the collection of output segments.Splitter shall operate on every one input segment and divide it into N output segments in the following manner:Every output buffer receives one output segment.Each input segment is divided into the N output segment with the following order of s0, s1, . . . , sN-1, where the segment sicovers the duration of media right after segment si−1and si+1.The output buffers are ordered as O0, O1, . . . , ON-1.The output segment siin placed in buffer Oi.
[Function Description Template]

The requirements assume that the input media stream is continuous without any gaps. In this clause, [a,b[ means a time range equal or greater than a and smaller than b.

The Merger Function shall have the following requirements:N input and one output FIFO buffers, where N is a configuration parameter for the number of splits.Operates one input segment from each input buffer, total N segments, at each time and generates one output segmentEach input segments shall satisfy the following requirements:continuous set of samplesmaximum duration of D in the scale of time-scale T, where D and T are a configuration parametersinclude the following metadata:Start time s (msec)time-scale tmaximum duration d in the scale oft, where d=D*T/t is an integer number i.e. D*T is divisible by t.length l (bytes)Non-overlapping samples with other input segmentsThe set of input segments of N buffer shall cover the entire duration of input, i.e. no sample of input is left out from the total duration.Operates on the input segments in incrementing order, i.e a segment covering an earlier duration of the time shall be processed before any segment covering later duration.The media streams at the output buffer at any time consist of zero or more output segments. Each output segment shall satisfy the following requirementscontinuous duration of samplesduration of N*d in the scale of tinclude the following metadata:Start time s (msec)time-scale of tduration N*dlength l (bytes)segment marker when variable-duration=‘True’, where segment markers indicated the starting time of each segmentThe collection of output segments of all output buffer together shall cover the entire duration of input, i.e. no sample of input is left out of the collection of output segments.Splitter shall operate on every one input segment from each input buffer and merge them into one output segment in the following manner:One segment from each input buffer is processed, s0, s1, . . . , sN-1, from input buffers I0, I1, . . . , IN-1, respectively. The segment siincludes samples of time interval exactly between the time intervals of si−1and si+1.Segment s0, s1, . . . , sN-1are concatenated in shown order into one output segment, such that the output segment includes all samples of segment s0, s1, . . . , sN-1and with no repeating samples, and with the earliest presentation of EPT.
[Function Description Template]

TABLE 16Merge configuration parametersValidNameDefinitionUnitTyperangemerge-Number of mergesN/Anumberunsignednumberinteger(non-zero)time-scaleThe timescale in units per second to beN/Anumberunsignedused for the derivation of different real-integertime duration values of mediasegments.If not present on any level, it shall beset to 1.segment-The duration of the operational segmentN/Anumberunsigneddurationin scale of time-scaleintegerIf variable-duration is ‘True’ , thisvalue indicates the maximum durationof the segment.variable-If ‘True’, the segment duration mayN/AN/Abooleandurationvary segment to segment.If ‘False’, every segment has a durationequal to segment-duration.in-buffer-sizeSize of each input FIFO buffers.bytenumberunsignedintegerout-buffer-sizeSize of the output FIFO buffer.bytenumberunsignedintegermax-segment-sizeMaximum size of operational segmentbytenumberunsignedintegernon-segment-opIf ‘true’, this implementation supportsN/AbooleanBooleannon-segment operationThe default is ‘false’.buffer-fullness-The percentage increase of bufferN/Anumberunsignedinc-eventfullness by which an event is issuedintegerbetween 1and 100unsigned integer = [0, (2*53) − 1]

TABLE 20{“name”: “buffer-fullness”,“definition”: “level of buffer fullness in percentage”,}

Embodiments may relate to a method for describing a segmentation of the media with variable duration segments wherein the media is processed by independent segments with variable durations, wherein the step descriptor is extended to signal the function's support for variable segment duration.

Embodiments may relate to a method for splitting and merging media streams to and from multiple media substreams wherein the split and merge function is performed on segments with variable durations wherein the boundaries of segments are defined and the process of split and merge are performed at these boundaries.

[Processing Multidimensional Media Segments on Cloud]

Embodiments may relate to a multi-dimensional media segment method for processing the media segments independently in the cloud. Embodiments may extend the NBMP standard's step descriptor to define multi-dimensional segments.

The NBMP standard includes a Step Descriptor that describes processing mode and temporal segment duration for each function as shown in Table 21.

TABLE 21NBMP Step DescriptorValidNameDefinitionUnitTyperangestep-moderunning mode with the following values:N/AstringN/A‘stream’: continuous execution‘stateful’: maintain the state of tasks atend each step‘stateless’: run in stateless modewithout the need for maintaining stateThe default value is ‘stream’.segment-durationduration for which the output(s) ofmicro-numberunsignedresource are independent to any inputssecondsintegeroutside of the corresponding duration.operation-unitsnumber of segment-duration the resource isN/Anumberunsignedoperating in a stateless fashioninteger
[General Concept]

Embodiments may extend the segment dimension to other dimensions that temporal axis. Table 22 shows an example of this extension.

TABLE 22multidimensional Step DescriptorValidNameDefinitionUnitTyperangestep-moderunning mode with the following values:N/AstringN/A‘stream’: continuous execution‘stateful’: maintain the state of tasks atend each step‘stateless’: run in stateless modewithout the need for maintaining stateThe default value is ‘stream’.segment-durationduration for which the output(s) ofmicro-numberunsignedresource are independent to any inputssecondsintegeroutside of the corresponding duration.operation-unitsnumber of segment-duration the resource isN/Anumberunsignedoperating in a stateless fashionintegertemporal-overlapdetermines the size of overlap betweenN/AarrayUnsignedsegments.integerThe default value is 0.higher-dimensions-The number of dimensions of segmentN/Anumberunsignedsizeother than temporal.integerThe default value is 2.largerthan 0higher-dimensions-The description of each other dimension.N/AN/AArray ofdescriptionsThe array size is equal to ‘other-stringsdimensions-size’. Each element is a string.The following values are defined:‘width’: width of media frame‘height’: height of media frame‘RGB’: color components R, Gand B‘depth’: non-depth and depth‘YUV’: color components Y, U, V‘V-PCC’: V-PCC componentspatch, geometry, occupancy andattributehigher-segment-sizeAn array defining the size of segment inN/AarrayArray ofthe higher dimensions. The array size isunsignedequal to ‘other-dimensions-size’. Eachnonzeroelement is unsigned non-zero integer.integersThe unit of size in each dimension dependson the unit of media on that dimension.For instance, the unit of dimension forspatial dimensions is pixel, and for colorcomponents is color component index.higher-overlapdetermines the size of overlap at eachN/AarrayArray ofdimension other than temporal.unsignedThe array size is equal to ‘other-integersdimension-size’. Each element is unsignedinteger.The default value is an array of 0s.higher-operation-number of segments size of the resource inN/AnumberArray ofunitseach dimension is operating in aunsignedstateless fashion.nonzeroThe array size is equal to ‘other-integersdimension-size’. Each element is unsignednonzero integer.The default value is an array of is.Example 1: For example, dividing a video frame into tiles for step processing can be achieved by the following configuration:higher-dimensions-size=2higher-dimensions-description=[width, height]higher-segment-size=[w, h] where w and h represent the width and height of each independent tile.Example 2: For a video with 3 color components:Higher-dimensions-size=1higher-dimensions-descriptions=[RGB]higher-segment-size=[c] where c represents the color component video.

The parameter overlap-size indicates the amount of overlap in each dimension. For instance, an overlap-size of [16 16] in Example 1 indicates an overlap of 16 samples in the width and height of the neighboring tiles.

The JSON implementation of the above concept is shown in Table 23.

Embodiments may relate to a method for describing a segmentation of multidimensional media signal to multidimensional segment by which each segment may be processed independently, wherein the dimension of the media segments and its properties, including size in each dimension and the amount of overlap in each dimension as well as the number of processing segments are described in a descriptor, wherein the descriptor can be used in a function description to describe the properties of the function or in a workflow or task description to describe the processing operation of the function in an operating workflow, by which allows independent or parallel processing of these multidimensional segments.

[Excluding Functions Instances and Tasks from Workflow Descriptors Configurations and Policies]

Embodiments may relate to a method for excluding function instances and tasks from one or more descriptors defined in a workflow description.

The NBMP amendment 1 includes the following method for the exclusion of functions:

The descriptor shall be included as a parameter scope to those descriptors defined in a WDD to indicate those tasks from which those descriptor parameters should be ignored as is shown in Table 24.

Table 26 defines the parameters used in Scope Descriptor.

The above implementation presents a number of problems. For example, the scope descriptor is used to exclude specific descriptors in WDD for a subset of Functions. It is used inside a Descriptor and is not a standalone descriptor. It applies to certain descriptors and not all. The possible descriptors to be used with are the security, requirements, reporting, and notification. It is not clear the function list is the names or identifier of the functions.

Accordingly, no clear list of descriptors is defined yet that the blackout list may be applied. It is not clear that the blackout is applied to a function, function instance/task in a workflow. A Workflow Description Document (WDD) may or may not include a workflow. If the WDD includes the workflow, it has a connection-map and possibly function-restrictions arrays of objects. If the WDD doesn't include the workflow, the descriptors are applied to the WDD and the functions and tasks are not defined yet. A Workflow Manager may choose different functions from one or more repositories to realize the workflow. The blackout list is a binary flag, meaning if a function is listed in the blackout list of the descriptor, the descriptor is not applied to a task implementing that function.

Because the function-restrictions list the restriction of each function in the workflow, embodiments may relate to adding the blacklist to that element.

For example, the following changes may be made:

Function Restrictionsa) Other descriptors may be added that are missing from the above table regardless of the blacklist feature.b) A blacklist may be added array to list the descriptors that are excluded for a function instance.

An example JSON implementation is shown in Table 28.

Embodiments may add a simple array of list of descriptors that are excluded for each function instance, as shown in Table 28.

This modification may provide several benefits. For example:1. It works on the function instance rather than a function. In a workflow, a function instance can be blacklisted and not all instances of that function.2. The function-restriction already lists different descriptors. If a descriptor is desired to be blackout for a function instance, it can be flagged in that array.3. Since blackout is a single parameter, there is no need of having a separate descriptor for defining it.4. If the WDD doesn't have the workflow, the WDD can derive the workflow and provide it back to NBMP in updated WDD. Then the NBMP source can add blackout flags to the desired function instances/tasks.

Embodiments may relate to method for excluding any descriptor in a Workflow Description Document (WDD) from a function instance wherein that function instance does not have to implement that specific descriptor, wherein each function instance may have own exclusion list, while other function instances of the same function may or may not have same exclusion wherein the NBMP Source can provide the exclusion list in the WDD when the workflow is provided, or if the workflow is not provided, the Workflow Manager can derive the workflow and provide it to NBMP Source and then the NBMP source updates the WDD with the exclusion list for each function instance/task and provide it back to the Workflow Manager to implement.

With reference toFIGS.3and8, a process800performed by the NBMP workflow manager320for split-rendering of a workflow is described below.

FIG.8is a flowchart is a flowchart of an example process800for reconstructing a current Network Abstraction Layer (NAL) unit for video decoding.

As shown inFIG.8, process800may include deriving a network based media processing (NBMP) workflow (block810).

As further shown inFIG.8, process800may include obtaining at least one first syntax element indicating that at least one task included in the NBMP workflow, at least one input received by the at least one task, or at least one output generated by the at least one task, is nonessential (block820).

As further shown inFIG.8, process800may include determining a plurality of essential tasks based on the at least one first syntax element (block830).

As further shown inFIG.8, process800may include assigning the plurality of essential tasks to at least one from among a media sink, a media source, and a media processing entity included in the NBMP workflow (block840).

In embodiments, the at least one first syntax element may include a first flag included in a general descriptor of the at least one task, and the first flag may indicate that the at least one task is nonessential. In embodiments, the first flag may correspond to nonessential flag of Table 1.

In embodiments, the plurality of essential tasks may be determined based on the first flag.

In embodiments, the at least one first syntax element may include a first parameter included in an output descriptor of the at least one output, and the first parameter may indicate that the at least one output is nonessential. In embodiments, the first parameter may correspond to the nonessentiality parameter of Table 3.

In embodiments, the plurality of essential tasks may be determined based on the first parameter.

In embodiments, process800may further include obtaining a step descriptor including at least one second syntax element indicating parallel processing information of at least one media segment associated with the NBMP workflow; and assigning the plurality of essential tasks based on the parallel processing information.

In embodiments, the at least one second syntax element may include a second flag indicating that durations of media segments may vary, and a second parameter indicating a maximum duration of the durations. In embodiments, the second flag may correspond to the element “variable duration” described above, and the second parameter may correspond to the element “segment-duration” described above.

In embodiments, the at least one second syntax element may include a third parameter that indicates at least one of a temporal overlap of the at least one media segment, a number of dimensions of the at least one media segment, a description of the dimensions of the at least one media segment, or an overlap of the dimensions of the at least one media segment. In embodiments, the third parameter may correspond to at least one of the parameters shown in Table 21.

In embodiments, process800may further include obtaining a function restrictions list corresponding to an instance of a function included in the NBMP workflow. In embodiments, the function restrictions list may indicate restrictions of a corresponding function. In embodiments, the function restrictions list may include a blacklist array that specifies descriptors excluded for the instance of the function in a workflow description document corresponding to the NBMP workflow.

In embodiments, the function restrictions list may be a first function restrictions list, and the instance of the function may be a first instance of the function, and a second function restrictions list corresponding to a second instance of the function may not include the blacklist array.

Further, the proposed methods may be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, the one or more processors execute a program that is stored in a non-transitory computer-readable medium to perform one or more of the proposed methods.

According to embodiments of the present disclosure, at least one processor with memory storing computer code may be provided. The computer code may be configured to, when executed by the at least one processor, perform any number of aspects of the present disclosure.

For example, with reference toFIG.9, computer code900may be implemented in the NBMP system300. For example, the computer code may be stored in memory of the NBMP workflow manager320and may be executed by at least one processor of the NBMP workflow manager320. The compute code may comprise, for example, workflow deriving code910, first obtaining code920, determining code930, and assigning code940.

The workflow deriving code910, first obtaining code920, determining code930, and assigning code940may be configured to cause the NBMP workflow manager320to perform the aspects of the process described above with reference toFIG.8, respectively.

Embodiments of the present disclosure may be used separately or combined in any order. Further, each of the embodiments (and methods thereof) may be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, the one or more processors execute a program that is stored in a non-transitory computer-readable medium.