Lossy Compression for Integrating Event Cameras

Andrew C. Freeman, Ketan Mayer-Patel

Abstract: Event cameras are biologically-inspired sensors that upend the framed, synchronous nature of traditional cameras. Singh et al. proposed a novel sensor design wherein incident light values may be measured directly through continuous integration, with individual pixels' light sensitivity being adjustable in real time, allowing for extremely high frame rate and high dynamic range video capture. Up to this point, there has been little research into compressing this event data, and even less that has investigated a robust method of lossy compression. This paper makes an inroad in this area, proposing a lossy model-based compression scheme with user-controlled quality levels and examine its compression efficiency and reconstructed image quality for several synthetic scenes. In our experiments, we observe space savings over the original event stream upwards of 95% with only minor losses in reconstructed image quality.