Boyko Y. Methods and models of information flows compression in decision support systems

Dissertation is devoted to the solution of important scientific and technical objective: improving the efficiency of information technology systems (ITS), based on the development of methods and models of adaptive data compression in decision support system. For the first time we developed a method and models adaptive source coding messages regularization limited loss of quality imaging. For the first time were developed the method and models of adaptive messages source coding with regularization of limited loss of quality imaging. Proposed a method of pre-quantization component transforms discrete cosine transform to the reduction in volume. Also was developed the method of decoding digital models and images in the pre-identification of recurrent characteristics of the structure adaptive mono positional numbers. On the basis of the developed methods and models offered improved compression technology of digital images in the ITS, which ensures the implementation of the functions of reducing redundancy in a limited loss of visual quality and reduce time delays in the processing of data in the decision support system. The proposed digital image compression technology ensures the implementation of the reduction of redundancy functions under limited losses imaging quality and reduce time delays in data processing, and achieves reduction in time to process and transfer an average of 20% by reducing the number of official data on the submission of information on the number of items in the columns array of lengths of series of binary elements.