  We apply reinforcement learning to video compressive sensing to adapt the
compression ratio. Specifically, video snapshot compressive imaging (SCI),
which captures high-speed video using a low-speed camera is considered in this
work, in which multiple (B) video frames can be reconstructed from a snapshot
measurement. One research gap in previous studies is how to adapt B in the
video SCI system for different scenes. In this paper, we fill this gap
utilizing reinforcement learning (RL). An RL model, as well as various
convolutional neural networks for reconstruction, are learned to achieve
adaptive sensing of video SCI systems. Furthermore, the performance of an
object detection network using directly the video SCI measurements without
reconstruction is also used to perform RL-based adaptive video compressive
sensing. Our proposed adaptive SCI method can thus be implemented in low cost
and real time. Our work takes the technology one step further towards real
applications of video SCI.
