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• W1968515662 abstract "Data compression techniques mainly consist of two operations, data compression itself and a consequent data de-compression. In real time, the compressor and de-compressor are causal and, at a given time, may process (or 'remember') only a fragment of the input signal. In the latter case, we say that such a filter has a finite memory. We study a new technique for optimal real-time data compression. Our approach is based on a specific formulation of two related problems so that one problem is stated for data compression and another one for data de-compression. A compressor and de-compressor satisfying conditions of causality and memory are represented by matrices with special forms, A and B, respectively. A technique for the solution of the problems is developed on the basis of a reduction of minimization problems, in terms of matrices A and B, to problems in terms of specific blocks of A and B. The solutions represent data compressor and data de-compressor in terms of blocks of those matrices that minimize associated error criteria. The analysis of the associated errors is also provided." @default.
• W1968515662 created "2016-06-24" @default.
• W1968515662 creator A5021819105 @default.
• W1968515662 creator A5047243168 @default.
• W1968515662 date "2010-10-01" @default.
• W1968515662 modified "2023-09-26" @default.
• W1968515662 title "Data compression under constraints of causality and variable finite memory" @default.
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• W1968515662 doi "https://doi.org/10.1016/j.sigpro.2010.04.001" @default.
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