| 摘要 |
Data compression for transfer (storage or communication) by a continuously adaptive probability decision model, closely approaches the compression entropy limit. Sender and receiver perform symmetrical compression/decompression of binary decision n according to probabilities calculated independently from the transfer sequence of 1 . . . n-1 binary decisions. Sender and receiver dynamically adapt the model probabilities, as a cumulative function of previously presented decisions, for optimal compression/decompression. Adaptive models for sender and receiver are symmetrical, to preserve data identity; transfer optimization is the intent. The source model includes a state generator and an adaptive probability generator, which dynamically modify the coding of decisions according to state, probability and bit signals, and adapt for the next decision. The system calculates probability history for all decisions, including the current decision, but uses probability history for decision n-1 (the penultimately current decision) for encoding decision n (the dynamically current decision). The system, separately at source and destination, reconfigures the compression/expansion algorithm, up to decision n-1, codes each decision in the data stream optimally, according to its own character in relation to the calculated probability history, and dynamically optimizes the current decision according to the transfer optimum of the data stream previously transferred. The receiver operates symmetrically to the sender. Sender and receiver adapt identically, and adapt to the same decision sequence, so that their dynamically reconfigured compression-expansion algorithms remain symmetrical-even though the actual algorithms may change with each decision as a function of dynamic changes in probability history.
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