Abstract: Video foreground and background detection is performed on a received video sequence on a macroblock by macroblock basis using motion vector encoding information associated with each macroblock. Some of the macroblocks in each frame, for example, edge or corner macroblocks, may be exempt from the foreground and background detection. The detection information, along with the video data, is passed to an image stabilization algorithm that prepares the video for subsequent video compression.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a broadcaster request to determine information for conducting a content broadcast through the computing system. One or more parameters for the broadcast can be determined using a machine learning model that has been trained to predict the one or more parameters based at least in part on data describing previously conducted broadcasts. Information that describes at least the one or more parameters is provided to the broadcaster.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a broadcaster request to determine information for conducting a content broadcast through the computing system. One or more parameters for the broadcast can be determined using a machine learning model that has been trained to predict the one or more parameters based at least in part on data describing previously conducted broadcasts. Information that describes at least the one or more parameters is provided to the broadcaster.

Abstract: A method and system for encoding a segment of an input video with accurate placement of key frames, e.g., I-Frames, using Group of Pictures (GOPs) length for output in a distributed video encoding system with two or more encoders is disclosed. The method includes creating overlapping segments in order to allow precise key frame placement. Further, the method includes using desired segment length and the desired key frame placement to create the overlapping segments. Further, the method includes skipping a certain number of frames at the beginning of the segment, and a certain number of frames at the end of the segment, thereby achieving accurate placement of key frames.

Abstract: A method and system for allocating a variable number of bits per frame in a distributed video encoding using a complexity analyzer is disclosed. The method includes receiving an input video for allocating the number of bits for each segment based on the complexity of the segment. Further, the method includes splitting the input video into plurality of segments. Further, the method includes determining the number of bits to be allocated to the plurality of segments based on the complexity measurement of the input video. In an embodiment, single complexity analyzer can be used to determine the complexity of the plurality of segments. In another embodiment, separate complexity analyzer can be used for each segment to determine the complexity. Further, the method includes allocating the bits to the plurality of segments of the input video. Further, the method includes combining the plurality of segments to form a single output video; thereby, obtaining the encoded output video.

Abstract: Video encoding operation is performed in a distributed video encoding system to meet a service layer agreement such as a target time for completion of the video encoding operation. Each of multiple videos being encoded is split into smaller jobs corresponding to video segments of the videos. The jobs are processed according to a job queue. The sequence of jobs in the job queue is periodically updated such that jobs corresponding to each video are processed within the corresponding target encoding time.

Abstract: Various of the disclosed embodiments relate to a distributed video encoding or transcoding system may utilize multiple encoding nodes to encode a video sequence by splitting the video into multiple smaller video segments. The assignment of video segments to the encoding nodes is performed to balance the use of the encoding nodes by selecting a node based on its encoding capabilities, e.g., whether the node employed a central processing unit (CPU) based encoding or a graphics processor unit (GPU) based encoding.

Abstract: Video is transmitted from a user device to a server in the network by minimizing the amount of time it takes to upload the video from the user device to the server while being encoded from a first video encoding format in which the video is stored at the user device to a second video encoding format specified by a user and/or by the server. Operational conditions such as fullness of a transmission buffer at the user device and the target quality of encoded video are used to control the video encoding operation.

Abstract: A distributed video encoding system splits an input video into video segments. The video segments are encoded using multiple video encoding nodes. Prior to the process of splitting the sequence into video segments, the video is analyzed to generate a dependency map. Intelligent segmentation is performed using the dependency map so that each video segment includes all the video frames from which other video frames within that segment have been encoded in the input video. For example, picture headers are inspected to determine the temporal distance of the farthest past and future reference frames used for encoding frames of a video.

Abstract: In a distributed video encoding system, a video is encoded by splitting into video segments and encoding the segments using multiple encoders. Prior to segmenting the video for distributed video encoding, image stabilization is performed on the video. For each frame in the video, a corresponding transform operation is first computed based on an estimated camera movement. Next, the video is segmented into multiple video segments and the corresponding per-frame transform information for the multiple video segments. The video segments are then distributed to multiple processing nodes that perform the image stabilization of the corresponding video segment by applying the corresponding transform. The results from all the stabilized video segments are then stitched back together for further video encoding operation.

Abstract: Various of the disclosed embodiments relate to multiple video encoders that are used to simultaneously encode a video using encoders configured using different encoding parameters. A segment selector selects an encoded version of the encoded video segment using operational criteria such as video quality and bandwidth. A configuration determination module may analyze the video segment to make a decision about which encoding parameter configurations may be suitable for encoding the video segment. The configuration determination module may be trainable, based on past encoding results.

Abstract: Various embodiments are generally directed to techniques to coordinate control of bitrates among multiple computing devices employed in parallel to transcode portions of a motion video. A device to coordinate parallel video transcoding includes a processor component; and a monitoring component for execution by the processor component to determine whether a total current bitrate remains within a target range of bitrates to transcode multiple segments of an original video data using multiple slave devices in parallel to generate a transcoded video data, the total current bitrate comprising a sum of current bitrates of video compression performed by the multiple slave devices in transcoding the multiple segments. Other embodiments are described and claimed.

Abstract: An apparatus may include a memory to store a video frame, a processor circuit and a selective encoding component for execution on the processor to perform selective encoding of the video frame, the selective encoding to classify the video frame into a primary object region and a background region, and encode the primary object region at a first quality level and the background region at a background quality level, the first quality level to comprise a higher quality level than the background quality level.