Abstract
Personalized delivery of multimedia content over the Internet opens new business perspectives for future multimedia applications and thus plays an important role in the ongoing MPEG-7 and MPEG-21 multimedia standardization efforts. Based on these standards, next-generation multimedia services will be able to automatically prepare the digital content before delivery according to the client's device capabilities, the network conditions, or even the user's content preferences. However, these services will have to deal with a variety of different end user devices, media formats, as well as with additional metadata when adapting the original media resources. In parallel, an increasing number of commercial or open-source media transformation tools will be available, capable of exploiting such descriptive metadata or dealing with new media formats; thus it is not realistic that a single tool will support all possible transformations.
In this paper, we present a novel, fully knowledge-based approach for building such multimedia adaptation services, addressing the above mentioned issues of openness, extensibility, and concordance with existing and upcoming standards. In our approach, the original media is transformed in multiple adaptation steps performed by an extensible set of external tools, where the construction of adequate adaptation sequences is solved in an Artificial Intelligence planning process. The interoperability issue is addressed by exploiting standardized Semantic Web Services technology. This technology allows us to express tool capabilities and execution semantics in a declarative and well-defined form. In this context, existing multimedia standards serve as a shared domain ontology.
The presented approach was implemented and successfully evaluated in an official ISO/IEC MPEG (Moving Picture Experts Group) Core Experiment and is currently under further evaluation by the standardization body.
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Jannach, D., Leopold, K., Timmerer, C. et al. A knowledge-based framework for multimedia adaptation. Appl Intell 24, 109–125 (2006). https://doi.org/10.1007/s10489-006-6933-0
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DOI: https://doi.org/10.1007/s10489-006-6933-0