Qualcomm Incorporated

Intuit, Inc.

Cisco Technology, Inc.

Ashish Kumar Mishra

Sandeep Nellikatte Srivatsa

Vikrant Mahajan

Manish Ramesh Shah

Jayanth Saimani

Vikash Kumar

Apoorva Nagarajan

Gopi Madhan Dabbadi

Harikrishna Madadi Reddy

Yunqing Chen

Aminish Sharma

Yasutomo Matsuba

Srikanth Alaparthi

Monimoy Deb Purkayastha

Krishna Murthy

Shengqi Yang

Mayank Jain

Darshan Deepak Digikar

Tushar Singhal

Anushrut Gupta

Arindam Mohanta

Goutham Kallepalli

Lingfeng Li

Rosali Patro

Narender Singh Pal

Divya Goswami

Anish Kumar

Systems and techniques are described herein for processing video data. For instance, a process can include obtaining a block of luma information for a block of video data. The process can further include applying a sample adaptive offset (SAO) filter to the luma information to generate SAO filtered luma information, inputting the SAO filtered luma information to a cross-component adaptive loop filter (CC-ALF) without storing the SAO filtered luma information to an external memory, applying the CC-ALF to the SAO filtered luma information to generate a first chroma CC-ALF output and a second chroma CC-ALF output, and storing the first chroma CC-ALF output and the second chroma CC-ALF output in the external memory.

Area optimized storage scheme for cross-component adaptive loop filtering

A device for upscale filtering video data includes one or more processors configured to generate a number of upscaled pixels that is an integer multiple of one half of a size of a filter used to upscale filter the video data. The device may then store the remaining pixels to be used to upscale filter a subsequent, right-neighboring block. In this manner, the device may avoid generating upscaled pixels that will result in being unaligned with the block and that may not be output due to being unaligned.

Upscale filtering of video data

Systems and methods for decoding are described. A decoder system is configured to retrieve, from the grain array stored within the on-chip memory, noise pixel data associated with at least one reconstructed video pixel. The decoder system is configured to apply noise data to the at least one reconstructed video pixel to generate at least one output video pixel. The noise data is determined based on the noise pixel data.

Systems and methods of video decoding with dynamic noise reconstruction

A method of automating a workflow may include obtaining a completeness graph including conditions applied to attributes of an entity, determining that a missing attribute subset of the attributes lacks a corresponding value, determining that a condition corresponding to a missing attribute of the missing attribute subset is satisfied, and obtaining a value for the missing attribute. The value may be a result of performing a task in the workflow. The method may further include modifying a state of the entity by assigning the value to the missing attribute to obtain a modified state of the entity.

System and method for leveraging a completeness graph

In one embodiment, an illustrative method herein may comprise: sending, from a server instrumentation agent configured on a transaction server, instrumented server performance data regarding the transaction server and an associated correlation token to an application programming interface (API) monitoring agent; receiving, at the server instrumentation agent, a returned acknowledgment of the instrumented server performance data regarding the transaction server and the associated correlation token from the API monitoring agent, the acknowledgement also having corresponding API monitored performance data regarding an API of the transaction server; returning, from the server instrumentation agent, a synchronization acknowledgment to the API monitoring agent in response to the returned acknowledgment to indicate complete synchronization; and correlating, by the server instrumentation agent, the instrumented server performance data regarding the transaction server and the API monitored performance data regarding the API of the transaction server based on the associated correlation token and complete synchronization.

Dual channel correlation of API monitoring to business transactions

Systems and techniques are provided for processing video data. For example, an apparatus may obtain one or more first sets of collocated motion vector data and one or more second sets of collocated motion vector data, associated with a respective first and second block of video data included in a current frame of video data. The apparatus may project the one or more first sets of collocated motion vector data into a first projected motion field associated with a first buffer and project the one or more second sets of collocated motion vector data into the first projected motion field associated with the first buffer. Based on projecting the one or more first sets and one or more second sets of collocated motion vector data, the apparatus may decode the first block of video data based on the first projected motion field associated with the first buffer.

Block-level collocated motion field projection for video coding

Video decoding systems and techniques are described. The decoder applies a deblocking (DB) filter to the plurality of sub-blocks of a block of video data to generate a DB-filtered plurality of sub-blocks. The decoder applies the DB filter to one or more lines (e.g., columns) of pixels in an additional sub-block of the block to generate a DB-filtered portion of the additional sub-block. The one or more lines of pixels in the additional sub-block are filtered without filtering an entirety of the additional sub-block using the DB filter. The additional sub-block is adjacent to at least one of the plurality of sub-blocks. The decoder applies a constrained directional enhancement filter (CDEF) to the DB-filtered plurality of sub-blocks and the DB-filtered portion of the additional sub-block to generate a CDEF-filtered plurality of sub-blocks.

Systems and methods for video decoding with partial-block deblocking filtering for use by constrained directional enhancement filter

Video decoding systems and techniques are described. A decoder reads first video data from a first block of a video frame. The decoder retrieves neighboring video data from a line buffer. The neighboring video data is from a neighboring block that neighbors the first block in the video frame. The decoder processes the first video data and the retrieved neighboring video data using a constrained directional enhancement filter (CDEF) to generate filtered first video data. The decoder upscales the filtered first video data using an upscaler to generate upscaled filtered first video data. The decoder upscales the retrieved neighboring video data using the upscaler to generate upscaled neighboring video data, for instance after generating the filtered first video data. The decoder processes the upscaled filtered first video data and the upscaled neighboring video data using a loop restoration (LR) filter to generate output video data.

Systems and methods of video decoding with improved buffer storage and bandwidth efficiency

A method may include obtaining a knowledge graph including entities, and determining, for the knowledge graph, a first state including a first selectable entity subset of the entities that are selectable by a user. The first selectable entity subset may include an entity. The method may further include receiving, from the user and via a graphical user interface (GUI), a selection of the entity from the first selectable entity subset, and responsive to the selection, adding the entity to a report schema. The method further includes restricting, by the GUI, a space of selectable entities addable to the knowledge graph to the first selectable entity subset. The report schema may be used to populate a report. The method may further include, responsive to the selection, transitioning the knowledge graph to a second state including a second selectable entity subset of the entities that are selectable by the user.

Custom report builder

A method may include obtaining a knowledge graph including entities, and determining, for the knowledge graph, a first state including a first selectable entity subset of the entities that are selectable by a user. The first selectable entity subset may include an entity. The method may further include receiving, from the user and via a graphical user interface (GUI), a selection of the entity from the first selectable entity subset, and responsive to the selection, adding the entity to a report schema. The report schema may be used to populate a report. The method may further include, responsive to the selection, transitioning the knowledge graph to a second state including a second selectable entity subset of the entities that are selectable by the user.

Certain aspects of the present disclosure provide techniques for scalably and efficiently converting linear image data into multi-dimensional image data for multimedia applications. In one example, a method for managing image data includes receiving a line of image data in a linear format via a system bus of width T, wherein the image data's native format is a tile format of H lines per tile; forming H subsets of image data from the line of image data in the linear format; writing the H subsets of image data to a memory comprising B=H banks of B=T/Bpixel width, wherein each subset of the H subsets is written to a different bank of the Bbanks; and outputting the H subsets of image data in the tile format.

Scalable and area efficient conversion of linear image data into multi-dimensional image data for multimedia applications

Techniques are described for performing transformation on video data. A transform circuit may receive M sample values of the video data from a pre-transform buffer, and process the M sample values with N computation units of the transform circuit to generate intermediate values. Processing the M sample values to generate the intermediate values includes feeding back temporary values from output of one or more of the N computation units to input of one or more of the N computation units. The transform circuit may store a first set of the intermediate values in a transpose buffer, and store a second set of the intermediate values in the pre-transform buffer that are to be later retrieved for storage in the transpose buffer.

Transform hardware architecture for video coding

As the quality and quantity of shared video content increases, video encoding standards and techniques are being developed and improved to reduce bandwidth consumption over telecommunication and other networks. One such technique for compressing videos involves transforming image data into an alternate, encoding-friendly domain (e.g., by a two-dimensional discrete cosine transform). Transform modules may be implemented to perform these transformations, which may occur during both video encoding and decoding processes. Provided are exemplary techniques for improving the efficiency and performance of transform module implementations.

Scalable transform hardware architecture with improved transpose buffer

As the quality and quantity of shared video content increases, video encoding standards and techniques are being developed and improved to reduce bandwidth consumption over telecommunication and other networks. One technique to reduce bandwidth consumption is intra-prediction, which exploits spatial redundancies within video frames. Each video frame may be segmented into blocks, and intra-prediction may be applied to the blocks. However, intra-prediction of some blocks may rely upon the completion (e.g., reconstruction) of other blocks, which can make parallel processing challenging. Provided are exemplary techniques for improving the efficiency and throughput associated with the intra-prediction of multiple blocks.

Pipelined intra-prediction hardware architecture for video coding

Local call information data sets are obtained for each of a plurality of devices engaged in a call, the local call information data sets comprising call attributes and associated metadata. Call signaling is mapped by correlating at least one attribute pertaining to the call in each of the local call information data sets. Global end-to-end call state machine is generated subsequent to mapping of call signaling. The global end-to-end call state machine includes a plurality of global states. Each global state represents a local state of each device along with state transition indicating change in the local state of at least one device.

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Bengaluru, India

Ashish Kumar Mishra