A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network
This addresses the problem of efficiently storing and transmitting event data for applications like robotics and surveillance, though it is incremental as it builds on existing compression methods with deep learning.
The paper tackles compressing neuromorphic vision data from event cameras by proposing a deep belief network-based scheme that reduces high-dimensional event data into a latent representation and encodes it with entropy coding, achieving a high compression ratio and outperforming state-of-the-art coders and lossless benchmarks.
A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the brightness change's time, location, and polarity. DVS consumes little power and can capture a wider dynamic range with no motion blur and higher temporal resolution than conventional frame-based cameras. Although this method of event capture results in a lower bit rate than traditional video capture, it is further compressible. This paper proposes a novel deep learning-based compression scheme for event data. Using a deep belief network (DBN), the high dimensional event data is reduced into a latent representation and later encoded using an entropy-based coding technique. The proposed scheme is among the first to incorporate deep learning for event compression. It achieves a high compression ratio while maintaining good reconstruction quality outperforming state-of-the-art event data coders and other lossless benchmark techniques.