Методи мінімізації обчислювальної надмірності при автоматизації конверсії потокових медіа-ресурсів

Abstract

Real-time conversion of dynamic streaming media encounters computational bottlenecks due to the continuous processing of redundant frames. This study proposes a three-tier context-aware adaptive filtering architecture to minimize load during the automated conversion of visual data into structured databases. Media streams are classified into dynamic interaction, passive standby, and target informational states. To eliminate redundancy, the system integrates hardware-driven state transitions, topological blocking with adaptive sampling, and state -predictive delays. These models significantly optimize resource consumption while preserving the semantic integrity of the extracted data.