Abstract
Due to the increased volume of data for image processing and transformation, there is a pressing need for storing the transmitted information. In this operation, two parameters are particularly important — the data packaging time and the degree of information compression. The studied compression methods have shown that improving one of these parameters leads to a significant deterioration of the other, as well as an increase in software or hardware costs. Therefore, depending on the specific situation, one has to sacrifice either the data packaging time or the size of the output data. Furthermore, existing data compression systems are poorly suited for image processing and do not take into account the possibility of utilizing data correlation in adjacent rows and columns, as well as the parallel flow of information from multiple rows or even the entire image at once.