A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network

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 time, location, and polarity of brightness change. DVS consumes little power and can capture a wider dynamic range with no motion blur and higher temporal resolution than conventional frame-based cameras. Despite yielding a lower bit rate compared to conventional video capture, the present approach of event capture demonstrates enhanced compressibility. Hence, we introduce a novel deep learning-based compression methodology tailored for event data. The proposed technique employs a deep belief network (DBN) to condense the high-dimensional event data into a latent representation, which is subsequently encoded utilising an entropy-based coding method. Notably, our proposed scheme represents one of the initial endeavours to integrate deep learning methodologies 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.