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With several advantages over conventional RGB cameras, event cameras haveprovided new opportunities for tackling visual tasks under challengingscenarios with fast motion, high dynamic range, and/or power constraint. Yetunlike image/video compression, the performance of event compression algorithmis far from satisfying and practical. The main challenge for compressing eventsis the unique event data form, i.e., a stream of asynchronously fired eventtuples each encoding the 2D spatial location, timestamp, and polarity (denotingan increase or decrease in brightness). Since events only encode temporalvariations, they lack spatial structure which is crucial for compression. Toaddress this problem, we propose a novel event compression algorithm based on aquad tree (QT) segmentation map derived from the adjacent intensity images. TheQT informs 2D spatial priority within the 3D space-time volume. In the eventencoding step, events are first aggregated over time to form polarity-basedevent histograms. The histograms are then variably sampled via Poisson DiskSampling prioritized by the QT based segmentation map. Next, differentialencoding and run length encoding are employed for encoding the spatial andpolarity information of the sampled events, respectively, followed by Huffmanencoding to produce the final encoded events. Our Poisson Disk Sampling basedLossy Event Compression (PDS-LEC) algorithm performs rate-distortion basedoptimal allocation. On average, our algorithm achieves greater than 6xcompression compared to the state of the art.

Lossy Event Compression based on Image-derived Quad Trees and Poisson Disk Sampling