Monday, April 13, 2015 - 11:30am - 12:20pm
Victoria Kostina (California Institute of Technology)
In data compression, a block of source symbols is compressed down into a smaller length string, while ensuring that the original block can be recovered from that encoded string, either exactly or approximately. Identifying the optimum rate-fidelity tradeoffs achievable in principle, regardless of the complexity of the code, is the subject of information-theoretic interest in data compression.
Tuesday, June 5, 2007 - 11:00am - 12:30pm
Ronald DeVore (University of South Carolina)
Best k-term approximation for bases and dictionaries, decay rates, approximation classes, application to image compression via wavelet decompositions.
Tuesday, September 24, 2013 - 2:00pm - 2:50pm
Alfredo Nava-Tudela (University of Maryland)
In recent years, interest has grown in the study of sparse solutions to underdetermined systems of linear equations because of their many potential applications. In particular, these types of solutions can be used to describe images in a compact form, provided one is willing to accept an imperfect representation. We shall develop this approach in the context of sampling theory, and for problems in image compression.
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