Flow-based visual stream compression for event cameras is essential in communication and power-constrained environments. The method introduced leverages optical flow estimates to predict future events, achieving significant compression ratios while maintaining low temporal errors. Various real-world datasets were evaluated, showcasing the effectiveness of the approach.
The content discusses the need for compressing output streams from neuromorphic, event-based vision sensors due to high data rates. A flow-based compression method is introduced, showing promising results in reducing data transmission while maintaining accuracy in predicting future events. The evaluation on different datasets demonstrates the efficiency and effectiveness of the proposed approach.
Key metrics such as compression ratio, event reduction, distance between event streams, and temporal error are used to evaluate the performance of the flow-based compression method across various scenarios. The results show significant improvements in data reduction while preserving reconstruction accuracy.
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arxiv.org
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