Lossless Compression of Neuromorphic Vision Sensor Data Based on Point Cloud Representation

Abstract

Visual information varying over time is typically captured by cameras that acquire data via images (frames) equally spaced in time. Using a different approach, Neuromorphic Vision Sensors (NVSs) are emerging visual capturing devices that only acquire information when changes occur in the scene. This results in major advantages in terms of low power consumption, wide dynamic range, high temporal resolution, and lower data rates than conventional video. Although the acquisition strategy already results in much lower data rates than conventional video, such data can be further compressed. To this end, in this paper we propose a lossless compression strategy based on point cloud compression, inspired by the observation that, by appropriately reporting NVS data in a $(x,y,t)$ tridimensional space, we have a point cloud representation of NVS data. The proposed strategy outperforms the benchmark strategies resulting in a compression ratio up to 30% higher for the considered dataset.

Keywords

• Neuromorphic vision sensor (NVS)
• neuromorphic spike events
• point cloud compression
• geometric point cloud compression (GPCC)
• silicon retinas
• spike encoding