Microsoft Technology Licensing, LLC

William David Sproule

Nitin Suri

Tzong-Jhy Wang

Andrew Shaun Ivory

W Michael Anderson

Bruce Alan Johnson

Sriram Subramanian

Bruce Justin Lindbloom

Antonio Winslow Fernando

Polita Huff

Mindy C King

Jin Li

Xian-Sheng Hua

Michael John Novak

Daniel F Plastina

Kipley J Olson

Andrew L Silverman

Giedrius Zizys

John Michael Thornton

Karthik G Anbalagan

Chunkit Jacky Chan

Scott Robert Ramsby

Alexander Sabo Brodie

Michael H Kim

Eric W Richards

Timothy R O'Connor

Eric Wayne Doerr

Bryan P Ferguson

Victor E Santodomingo

Omkar Anand Mehendale

Ryan A Kabir

Samuel J Pinson

Aanand Narayanan Pisharathu

Shanjeef Satchithanantham

Shawn Lee Morrissey

Bryan Ballinger

Kim Obbink

Mike W Morrison

The importance of video sections of a video file may be determined from features of the video file. The video file may be decoded to obtain video frames and audio data associated with the video frames. Feature scores for each video frame may be obtained by analyzing features of the video frame or the audio data associated with the video frame based on a local rule, a global rule, or both. The feature scores are further combined to derive a frame importance score for the video frame. Based on the feature scores of the video frames in the video file, the video file may be segmented into video sections of different section importance values.

Rule-based video importance analysis

Technologies for a single-pass process for enhancing video quality with temporal smoothing. The process may include providing for user overrides of automatically enhanced video/frame characteristics and providing substantially immediate previews of enhanced video frames to a user. The process may also include detecting a degree of shakiness in a portion of the video, and performing or recommending stabilization based on the detected shakiness.

Automatic video quality enhancement with temporal smoothing and user override

Techniques for identifying prominent subjects in video content based on feature point extraction are described herein. Video files may be processed to detect faces on video frames and extract feature points from the video frames. Some video frames may include detected faces and extracted feature points and other video frames may not include detected faces. Based on the extracted feature points, faces may be inferred on video frames where no face was detected. The inferring may be based on feature points. Additionally, video frames may be arranged into groups and two or more groups may be merged. The merging may be based on some groups including video frames having overlapping feature points. The resulting groups each may identify a subject. A frequency representing a number of video frames where the subject appears may be determined for calculating a prominence score for each of the identified subjects in the video file.

Computerized prominent character recognition in videos

Enhanced, efficient source frame decoding for user previewing is implemented by decoding and caching source frames of an input file that a user is interested in. Source frames for a user preview session are identified and decoded first to enhance user satisfaction with more timely preview segments for review. Additional source frames continue to be decoded on the fly to opportunistically enhance the current preview segment and to be prepared for additional preview segments and/or output file generation.

Opportunistic frame caching

This disclosure describes techniques for training models from video data and applying the learned models to identify desirable video data. Video data may be labeled to indicate a semantic category and/or a score indicative of desirability. The video data may be processed to extract low and high level features. A classifier and a scoring model may be trained based on the extracted features. The classifier may estimate a probability that the video data belongs to at least one of the categories in a set of semantic categories. The scoring model may determine a desirability score for the video data. New video data may be processed to extract low and high level features, and feature values may be determined based on the extracted features. The learned classifier and scoring model may be applied to the feature values to determine a desirability score associated with the new video data.

Computerized machine learning of interesting video sections

A device virtualization service (DVS) is provided that uses a generalized thick client resident on a mobile device to provide user interface generation support to a myriad of services providing mobile device content. The DVS abstracts device specifics from services to provide device independent user experiences to be described by the service and then rendered on the device.

Virtualization of mobile device user experience

Visual animation platforms may allow users to develop visual media projects, such as movies. Many visual animation platforms may provide animation effects that may be applied to visual elements of a visual media project. Unfortunately, current techniques for providing a preview of an animation effect may be limited. Accordingly, one or more systems and/or techniques for presenting a visual preview are disclosed herein. In particular, a snapshot of an original state of a selected visual element may be stored. A referenced animation effect may be applied to the selected visual element to generate an updated visual element that may be used to generate a visual preview of how the referenced animation effect may look as applied to the selected visual element. The snapshot may be applied to the updated visual element to non-destructively revert the updated visual element to the original state.

Visual previews

Automatic story production is implemented by the utilization of theme scripts with user assets to generate a quality finished product with minimum user input or direction. A user chooses a predesigned theme script to be applied to the user's assets to automatically create a story with a particular look and feel. Metadata and feature information, when available, is automatically gathered from the user assets to personalize the generated story. A user can include additional information and/or alter any aspect of the generated story to further personalize the resultant finished product.

Automated story generation

A global tag for an image is received identifies one or more objects in the image, and a region-specific tag for the image identifies one or more objects in a region of the image. The global tag and the region-specific tag are stored with the image. Displayed, along with the image, is an identifier for each of the one or more objects identified in the global tag, and an identifier for each of the one or more objects identified in the region-specific tag. Different users are able to maintain different names for the same person, allowing the same tag of the image to be used as the basis for displaying the image with different names for the different users. Additionally, the tags can be used as a basis for generating a credits list of people that are included in a compilation of images.

Image tagging user interface

In an implementation, a plurality of media is collected, each having associated metadata that describes respective media. A movie is made from the plurality of media having a credit sequence and a title sequence that are generated automatically and without user intervention from the metadata.

Movie making techniques

A media browsing system prepares various media content and synchronizes the content for playback. A user can specify the content and preconfigure the manner in which the content will be subsequently accessible through a user interface. A variety of mobile phone and media player devices having configurable, user interfaces promote a consistent media browsing experience across the devices for users accessing a variety of preconfigured media content.

Data preparation for media browsing

A system is described which includes a content retriever to retrieve and format data and a media file playlist generated by the content retriever from the data. The media file playlist includes a playlist entry title identifying a title for a media file, a file name identifying the media file, and a metadata tag containing data for performing an action associated with the file. Examples of the actions that may be performed are calling a phone number or sending an email. The system also includes a text file playlist generated by the content retriever from the text data in XML (extensible markup language). The text file play list includes text-to-speech markup tags indentifying the XML-formatted text file as a text-to-speech conversion file. The system further includes a text-to-speech converter configured to generate at least one audio file playlist from the text file playlist and a configuration module associated with the content retriever configured to accept user input instruction that identify the data.

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Woodinville, WA, United States of America

William David Sproule