Next generation network and operating system requirements for continuous time media
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
KeywordsMultimedia Object Motion Video Frame Header Subjective Point Playback Rate
- 1.“A Construction Set for Multimedia Applications,” Matthew E. Hodges, Russell M. Sasnett, and Mark S. Ackerman, IEEE Software, January 1989Google Scholar
- 2.“Intermedia: The Concept and the Construction of a Seamless Information Environment,” Nicole Yankelovich, Bernard J. Haan, Norman K. Meyrowitz, and Steven M. Drucker, Brown University, IEE Computer, January 1988Google Scholar
- 3.Hadamard, J., The Psychology of Invention in the Mathematical Field. Princeton University Press, 1945Google Scholar
- 4.“The Effect of VDU Text-Presentation Rate on Reading Comprehension and Reading Speed,” Jo. W. Tombaugh, Michael D. Arkin, and Richard F. Dillon, Proceedings of ACM CHI '85 Conference of Human Factors in Computing Systems, 1985Google Scholar
- 5.“Intelligent Interactive Video Simulation of a Code Inspection,” Scott M. Stevens, Communications of the ACM, July 1989 Volume 32 Number 7Google Scholar
- 6.Kraft, R. Mind and media: The psychological reality of cinematic principles. In Images, Information & Interfaces: Directions for the I990's, D. Schultz and C.W. Moody, Eds. Human Factors Society, New York, 1988Google Scholar
- 7.“Cinematic Primitives for Multimedia,” Glorianna Davenport, Thomas Aguierre Smith, and Natalio Pincever, IEEE Computer Graphics & Applications, July 1991Google Scholar
- 8.“Parsing Movies in Context,” Thomas G. Aguierre Smith and Natalio C. Pincever, USENIX-Summer '91, Nashville, TNGoogle Scholar