  Camera sensors have been widely used in intelligent robotic systems.
Developing camera sensors with high sensing efficiency has always been
important to reduce the power, memory, and other related resources. Inspired by
recent success on programmable sensors and deep optic methods, we design a
novel video compressed sensing system with spatially-variant compression
ratios, which achieves higher imaging quality than the existing snapshot
compressed imaging methods with the same sensing costs. In this article, we
also investigate the data transmission methods for programmable sensors, where
the performance of communication systems is evaluated by the reconstructed
images or videos rather than the transmission of sensor data itself. Usually,
different reconstruction algorithms are designed for applications in high
dynamic range imaging, video compressive sensing, or motion debluring. This
task-aware property inspires a semantic communication framework for
programmable sensors. In this work, a policy-gradient based reinforcement
learning method is introduced to achieve the explicit trade-off between the
compression (or transmission) rate and the image distortion. Numerical results
show the superiority of the proposed methods over existing baselines.
