Real time video systems play a significant role in many fields of science and technology. The range of their applications is constantly increasing together with requirements to them, especially it concerns to real time video systems with the feedbacks. Conventional fundamentals and principles of real-time video systems construction are extremely redundant and do not take into consideration the peculiarities of real time processing and tasks, therefore they do not meet the system requirements neither in technical plan nor in informational and methodical one. Therefore, the purpose of this research is to increase responsiveness, productivity and effectiveness of real time video systems with a feedback during the operation with the high-speed objects and dynamic processes. The human visual analyzer is considered as a prototype for the construction of intelligent real time video systems. Fundamental functions, structural and physical peculiarities of adaptation and processes taking place in a visual analyzer relating to the information processing, are considered. High selectivity of information perception and wide parallelism of information processing on the retinal neuron layers and on the higher brain levels are most important peculiarities of a visual analyzer for systems with the feedback. The paper considers two directions of development of intelligent real time video systems. First direction based on increasing intellectuality of video systems at the cost of development of new information and dynamic models for video information perception processes, principles of control and reading parameters of video information from the sensor, adapting them to the requirements of concrete task, and combining of input processes with data processing. Second direction is associated with the development of new architectures for parallel perception and level-based processing of information directly on a video sensor matrix. The principles of annular and linear structures on the neurons layers, of close-range interaction and specialization of layers, are used to simplify the neuron network.
Specific read only data management for memory hierarchy optimization