Ilyash Y. Methods and Tools for Adaptive Data Compression Based on Code Recurrent Sequences

Object of research - processes of adaptive compression of data streams based on recursive code sequences, purpose - methods and tools for adaptive compression of data streams that implement recursive properties of code sequences Galois; novelty - methods of reducing the redundancy of information flows that formed deterministic and random signals based on first-order processes prediction and interpolation methods, and the formation of service data elements recursive code sequences in theoretical and numerical basis Galois, which helped to improve the compression ratios of information in comparison with known methods, developed recurrent-adaptive method of reducing redundancy quasistationary information flows, and obtained objective function approximation algorithms compression data by identifying priority method that allowed optimize compression and define the basic theoretical and algorithmic basis for creation of the Special Processors, formulated evaluation methods for information compression and established a system of uniform numerical characteristics for comparing algorithms reduce redundancy among themselves that possible to improve the characteristics of the developed adaptive-recursive methods by minimizing the amount of proprietary information; improved method of adaptive redundancy reduction information based on the prediction of zero order, which differs from the known coded quantization levels recurrent elements of code sequences, allowing us to simultaneously reduce redundancy and improve zavadozahyschenist, was further development criterion evaluating the effectiveness of reducing redundancy at different significant number of samples and the sampling frequency, allowing us to set the boundaries of the positive effect of reducing the redundancy of information flows for existing and developed methods.