Towards Efficient Neuromorphic Data Processing: A Novel Representation with Lossless Spatio-Temporal Compression

Dynamic Vision Sensors (DVS) asynchronously capture brightness changes at pixel-level precision, enabling high temporal resolution, wide dynamic range and low power consumption. To sustainably manage redundancy in event data, we introduce a novel lossless compression framework that exploits DVS-specific characteristics through two core representations: the Super Binary Map (SBM) and the Temporal Event Vector (TEV). SBM is a voxelized binary structure capturing the inherent spatio temporal sparsity and polarity of events, compressed effectively using Run-Length Encoding (RLE). TEV complements this by precisely encoding each event's timing into compact, variable-length vectors, optimized through a context-adaptive entropy coder inspired by Markov models. This spatio temporal representation substantially enhances compression efficiency. Our approach significantly outperforms conventional methods, achieving improvements up to 52 x over AVC, 32 x over HEVC, and 9 x over VVC at fine temporal scales. At ultra-fine granularity (10(-4)s or 10,000 fps), our method attains remarkable compression ratios (up to 307x), vastly exceeding traditional approaches (e.g., LZMA, Brotli, Zlib). This consistent and substantial margin highlights the method's adaptability to dynamic and static scenes, offering a scalable, domain-specific solution for efficient lossless compression of event-based vision data.