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Next generation network and operating system requirements for continuous time media

  • Scott M. Stevens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 614)

Abstract

Accessing massive multimedia databases will require multiple representations of those databases. Initial access may be through visual representations of the database. However, traversing numerous levels of tree-like structures will quickly find the user lost Simple database queries may overwhelm users with information.

To overcome these problems the Advanced Learning Technologies Project at Carnegie Mellon University's Software Engineering Institute embeds in multimedia objects the knowledge of the content of those objects over several dimensions. With this model, variable granularity knowledge about the domain, content, image structure, and the appropriate use of content and image is embedded with the object. In ALT, a rule base acts as a visual director, making a judgement on what image to display and how to manipulate it. This provides the ability to present disparate text, audio, images, and video, intelligently in response to users needs.

It is difficult to move through information that has an intrinsic and essentially fixed temporal element such as video. While detailed indexing of video can help, users often wish to peruse video much as they flip through the pages of a book. Two techniques developed for this project will facilitate such searches. First, detailed, embedded knowledge of the video information will allow for scans by various views, such as by content area or depth of information. Second, partitioning multimedia data into smaller objects reducing bandwidth problems associated with accessing central data in large video files. Concatenation of logically contiguous files allows for seamless, continuous play of long sequences

Keywords

Multimedia Object Motion Video Frame Header Subjective Point Playback Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Scott M. Stevens
    • 1
  1. 1.Software Engineering InstituteCarnegie Mellon UniversityUSA

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