Flow-Based Visual Stream Compression for Event Cameras
Daniel C. Stumpp, Himanshu Akolkar, Alan D. George, Ryad Benosman

TL;DR
This paper presents a real-time, flow-based compression method for event camera streams that significantly reduces data transmission needs while maintaining low latency and accuracy, suitable for bandwidth-constrained environments.
Contribution
The paper introduces a novel flow-based compression technique leveraging optical flow estimates for real-time event stream prediction and compression, outperforming existing methods in efficiency.
Findings
Achieves an average compression ratio of 2.81 with low temporal error.
Combines with LZMA to reach ratios up to 17.24.
Demonstrates real-time, low-latency event prediction.
Abstract
As the use of neuromorphic, event-based vision sensors expands, the need for compression of their output streams has increased. While their operational principle ensures event streams are spatially sparse, the high temporal resolution of the sensors can result in high data rates from the sensor depending on scene dynamics. For systems operating in communication-bandwidth-constrained and power-constrained environments, it is essential to compress these streams before transmitting them to a remote receiver. Therefore, we introduce a flow-based method for the real-time asynchronous compression of event streams as they are generated. This method leverages real-time optical flow estimates to predict future events without needing to transmit them, therefore, drastically reducing the amount of data transmitted. The flow-based compression introduced is evaluated using a variety of methods…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Scientific Computing and Data Management
