METHOD FOR DETERMINING THE INFORMATIVENESS OF SEGMENTS FOR INTELLI-GENT VIDEO CONTENT PROCESSING SYSTEMS - Barannik, Vladimir; Pertsev, Pavlo (Science-based technologies, 2026)
The article shows that currently a number of effective modern video data encoding technologies have been created, which are based on standardized video frame compression formats and dynamic streams. These include: H265/HEVC and H266/VVC. Accordingly, a number of directions are outlined for taking into account the features (complexity) of the content for these technologies. The first type of approaches is based on the principle of reducing the bitrate while complying with the requirements for the level of root-mean-square indicators, which determine the level of integrity. The disadvantages here are: increasing the complexity of processing due to the multi-iterative process of constructing a syntax tree; the possibility of making only a rough estimate of the bitrate level before the compression process (entropy coding) begins. The second direction is based on approaches based on the use of artificial intelligence models. At the same time, this approach has certain limitations. This concerns the increase in the complexity of processing and time delays in the process of learning and forming a latent space. In addition, errors occur in the process of identifying objects. One of the effective directions for increasing the efficiency of semantic selection of video segments in the compression process is the development of combined approaches. In this case, the basic one is the identification (classification) of segments by the level of semantic complexity on the basis of a set of features that are detected at the level of syntactic perception of video information. The main task here is the selection of such features that will simultaneously have an associative dependence with the level of semantic complexity of video segments and the permissible energy efficiency of the computational process. Methodological foundations have been developed for the threshold-metric classification of standardized segments by size into two basic classes based on the scaling of the results of division into classes of its local segments. The basic ones here are: squaring the segment by local segments with the formation of an information model for each of them by a set of structural and statistical parameters; Scaling to establish the level of complexity of a video image segment from the perspective of the potential of its features with respect to the presence of redundancy is carried out on the basis of a decision rule based on the analysis of frequency information of the results of dividing local segments into two basic classes; determination of the complexity class of local segments based on relating the metric to one of two permissible threshold intervals with a defined limit.