Dynamic and active pixel vision sensors (DAVISs) are a new type of sensor that combine a frame-based intensity readout with an event-based temporal contrast readout. This paper demonstrates that these sensors inherently perform high-speed, video compression in each pixel by describing the first decompression algorithm for this data. The algorithm performs an online optimization of the event decoding in real time. Example scenes were recorded by the 240×180 pixel sensor at sub-Hz frame rates and successfully decompressed yielding an equivalent frame rate of 2kHz. A quantitative analysis of the compression quality resulted in an average pixel error of 0.5DN intensity resolution for non-saturating stimuli. The system exhibits an adaptive compression ratio which depends on the activity in a scene; for stationary scenes it can go up to 1862. The low data rate and power consumption of the proposed video compression system make it suitable for distributed sensor networks.
Real-time, high-speed video decompression using a frame- and event-based DAVIS sensor
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