Library-based coding: a representation for efficient video compression and retrieval

Abstract

The ubiquity of networking and computational capacity associated with the new communications media unveil a universe of new requirements for image representation. Among such requirements is the ability of the representation used for coding to support higher-level tasks such as content-based retrieval. We explore the relationships between probabilistic modeling and data compression to introduce a representation-library-based coding-which, by enabling retrieval in the compressed domain, satisfies this requirement. Because it contains an embedded probabilistic description of the source, this new representation allows the construction of good inference models without compromise of compression efficiency, leads to very efficient procedures for query and retrieval, and provides a framework for higher level tasks such as the analysis and classification of video shots.