The disclosed computer-implemented method may include (1) accessing a first media data object and a different, second media data object that, when played back, each render temporally sequenced content, (2) comparing first temporally sequenced content represented by the first media data object with second temporally sequenced content represented by the second media data object to identify a set of common temporal subsequences between the first media data object and the second media data object, (3) identifying a set of edits relative to the set of common temporal subsequences that describe a difference between the temporally sequenced content of the first media data object and the temporally sequenced content of the second media data object, and (4) executing a workflow relating to the first media data object and/or the second media data object based on the set of edits. Various other methods, systems, and computer-readable media are also disclosed.

Methods and apparatus to estimate audience sizes using deduplication based on vector of counts sketch data are disclosed. An example apparatus to determine an audience size for media based on vector of counts sketch data includes: a coefficient analyzer to determine coefficient values of a polynomial based on variances, a covariance, and cardinalities corresponding to a first vector of counts from a first database and a second vector of counts from a second database; an overlap analyzer to determine a real root of the polynomial, the real root corresponding to an estimate of an overlap between the first vector of counts and the second vector of counts; and a report generator to estimate the audience size based on the estimate of the overlap and the cardinalities of the first vector of counts and the second vector of counts.

In various embodiments, a training application trains a machine learning model to preprocess images. In operation, the training application computes a chroma sampling factor based on a downscaling factor and a chroma subsampling ratio. The training application executes a machine learning model that is associated with the chroma sampling factor on data that corresponds to both an image and a first chroma component to generate preprocessed data corresponding to the first chroma component. Based on the preprocessed data, the training application updates at least one parameter of the machine learning model to generate a trained machine learning model that is associated with the first chroma component.

The present disclosure describes a content consumption monitor (CCM) that determines surges in content consumption based on changes in content consumptions scores. The CCM determines the content consumptions scores for domains and/or organizations (orgs) based on session events generated by different devices/users from the org and/or domain, a number of events generated by the org/domain, content and/or user interactions with the content indicated by the events, relevancy scores of the content to one or more topics, and/or other criteria. The CCM detects surges in consumption or interest in a topic for the domain/org when the consumption score reaches a threshold and/or within a period of time. The CCM may adjust the consumption score based on the changes in the relevancy, number of events and/or the number of users over different time periods. Other embodiments may be described and/or claimed.

A computer implemented method that includes receiving first user usage data and second user usage data indicative of user usage during consumption of a first content and of a second content, respectively. The method includes determining a first user usage pattern for the first content and a second user usage pattern for the second content based on the first user usage data and on the second user usage data, respectively. The method determines potential content overlap between the first content and the second content based on a comparison of the first usage pattern and the second usage pattern and performs an optimization operation based on the potential content overlap.

The present application provides a method of processing a video stream. The method includes: acquiring a plurality of video streams for a live-streaming event; determining respective feature information of the plurality of video streams; determining a feature information of a client in response to a video request message from the client; matching the respective feature information of the plurality of video streams with the feature information of the client respectively, so as to obtain respective matching degrees of the plurality of video streams with respect to the client; and selecting one video stream from the plurality of video streams to obtain a selected video stream according to the respective matching degrees of the plurality of video streams with respect to the client, and pushing the selected video stream to the client. The present application further provides a computer device, and a medium.

This application provides a live streaming processing method performed by an electronic device. The method includes: displaying a live streaming room, the live stream room having a host account, a host sub-account and multiple viewer accounts, the host sub-account being used for assisting the host account of the live streaming room in operation; receiving real-time live streaming data of the live streaming room, and displaying a live streaming content on a live streaming room page according to the real-time live streaming data, the real-time live streaming data collected from the host account and the viewer accounts; and displaying, in response to a live streaming room operation of the host sub-account, an operation result of the live streaming room operation of the host sub-account in the live streaming room, wherein the operation result of the live streaming room operation updates the live streaming content on the live streaming room page.

Methods and systems are provided for bitrate adaptation of a video asset to be streamed to a client device for playback. The method includes selecting a representation from a manifest which expresses a set of representations available for each chunk of the video asset and generating a dynamic manifest for the video asset in which the representation selected for the at least one chunk is recommended for streaming to the client device. The selection of the representation recommended for the chunk may be based on at least one of historic viewing behavior of previous viewers of the chunk, content analysis information for the chunk, a level of available network bandwidth, a level of available network storage, and data rate utilization information of network resources including current, average, peak, and minimum data rate of network resources.

One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.

A system and method for media-object binding and predicting performance in a media is provided. One or more sets of object data may be associated with the media. One or more media-object bindings between the one or more sets of object data and the media may be formed and stored in one or more databases. The one or more sets of object data may include data about an object displayed during at least the portion of the media. One or more object-object associations between the one or more sets of object data and other stored object data may be formed and stored the one or more databases. The one or more object-object associations may form one or more object aggregations. A determination may for made regarding if any of the one or more object aggregations meet or pass a threshold.