Generative Multimedia (GM) applications are an increasingly popular way to
implement interactive media performances.
Our contributions include creating a metric for evaluating Generative
Multimedia performances, designing a model for accepting perceiver
preferences, and using those preferences to adapt GM performances.
The metric used is imprecision, which is the ratio of the
actual computation time of a GM element to the computation time of a
complete version of that GM element.
By taking a perceiver's
preferences into account when making adaptation decisions, applications
can produce
GM performances that meet soft real-time
and resource constraints while allocating imprecision to the GM elements
the perceiver least cares about.
Compared to other approaches, perceiver-directed imprecision best allocates
impreciseness while minimizing delay.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7492 |
| Date | 15 September 2005 |
| Creators | Jeff, Byron A. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1840882 bytes, application/pdf |
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