Provided are an electronic device and an operation method thereof. The electronic device includes a memory that stores one or more instructions, and a processor that executes the one or more instructions stored in the memory, wherein the processor is configured to execute the one or more instructions to: divide original image data into a plurality of image sequences; determine a predetermined number of image sequences among the plurality of image sequences as an input image group, select one of the image sequences included in the input image group and add the selected image sequence to the highlight image group based on one or more image sequences pre-classified as a highlight image group, by using a trained model trained using an artificial intelligence algorithm; and generate summary image data extracted from the original image data, by using the image sequence included in the highlight image group.

In various embodiments, a method for processing video streams is described. A plurality of video streams for transmission to a display device are received. The plurality of video streams have respective initial image quality levels. An estimated gaze location of a user of the display device is estimated. At least one video stream of the plurality of video streams is processed to have a modified image quality level based on the estimated gaze location. The modified image quality level is less than a corresponding initial image quality level. The plurality of video streams are transmitted to the display device.

Exemplary embodiments are directed to a device and method for controlling ambiance based on user-requested content. The device receives training data, user content requests, and user network device modification data. The device ascertains a content type for a user content request, performs analytics on the training data to create a model to predict ambiance settings, predicts ambiance settings for the content type using the model, evaluates the accuracy of the model and performs optimizations to the model to improve the predictions of the ambiance settings. The device controls the operating settings of one or more network devices based on the predicted ambiance settings. Moreover, exemplary embodiments are directed to controlling the enabling of display notifications, controlling reminder notifications, and controlling greeting notifications using a face identifier and notification settings.

Video streams uploaded to a video hosting platform are transcoded using quality-normalized transcoding parameters dynamically selected using a learning model. Video frames of a video stream are processed using the learning model to determine bitrate and quality score pairs for some or all possible transcoding resolutions. The listing of bitrate and quality score pairs determined for each resolution is processed to determine a set of transcoding parameters for transcoding the video stream into each resolution. The bitrate and quality score pairs of a given listing may be processed using one or more predefined thresholds, which may, in some cases, refer to a weighted distribution of resolutions according to watch times of videos of the video hosting platform. The video stream is then transcoded into the various resolutions using the set of transcoding parameters selected for each resolution.

Provided is an information processing device for effectively using a television that is in a non-use state by using an artificial intelligence function.

An information processing device for controlling operation of a display device by using an artificial intelligence function, includes an acquisition section that acquires sensor information, and an inferring section that infers content, which is to be outputted by the display device according to a use state, by using the artificial intelligence function, on the basis of the sensor information. The inferring section infers content, which is to be outputted by the display device that is in a non-use state, on the basis of information regarding a room where the display device is placed. The information regarding the room is included in the sensor information.

Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

The present disclosure discloses a content delivery method and system through an in-vehicle network based on regional content popularity, and in particular relates to the technical field of in-vehicle networks. For a content library corresponding to a target region and each target vehicle in the target region, each target vehicle responds to content objects in the content library. Based on each content object in the content library corresponding to the target region and a data eigenvalue of a preset data type corresponding to each content object, for each target vehicle in the target region that receives a data screening request, in response to the data screening request, content objects that match the data screening request are obtained and scheduled. A set of data screening requests responded to by each target vehicle is obtained by constructing a utility function.

Provided are techniques for dynamically creating and playing a scenario replication for an activity on one or more visual and audio devices. One or more patterns and one or more contexts are stored. One or more goals are stored for a user. A current context is identified from data from a plurality of data sources. The current context is matched to a stored context of the one or more contexts. A pattern associated with the matched context is identified. A recommendation of an activity is provided based on the pattern and based on a goal of the one or more goals. A scenario replication is created with at least one of visual elements and audio elements for the recommendation. One or more visual and audio devices are identified. The scenario replication is played on the one or more visual and audio devices.

An electronic device, for each media content item of a plurality of media content items, receives a respective score for each a first set of objectives and one or more other objectives and generates a respective score between a user and the media content item. The generating includes applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for a second set of objectives, wherein the second set of objectives includes (i) a resulting objective corresponding to the first set of objectives and having the first combined score and (ii) the one or more other objectives. The electronic device streams media content to the user selected based on the respective scores between the user and the media content items.

A video quality evaluation system comprises a training module, a calculation module, an analytical module, a designing module, an optimization module, and an estimation module. The training module collects training videos and trains labels of perceptual quality generation that is associated with the collected videos. The calculation module determines objective metrics based on the trained labels that are associated with the collected videos. The analytical module analyses scenes of the training video, and correlates objective metrics associated with the analysed scenes using perceptual parameters. The designing module designs a Convolutional Neural Network (CNN) Architecture based on data associated with the objective metrics and trains a model generated based on the designed CNN architecture. The optimization module optimizes the model and optimizes power after the model optimization. The estimation module estimates perceptual quality scores for incoming video data after the power optimization.