  Video block compressive sensing has been studied for use in resource
constrained scenarios, such as wireless sensor networks, but the approach still
suffers from low performance and long reconstruction time. Inspired by
classical distributed video coding, we design a lightweight encoder with
computationally intensive operations, such as video frame interpolation,
performed at the decoder. Straying from recent trends in training end-to-end
neural networks, we propose two algorithms that leverage convolutional neural
network components to reconstruct video with greatly reduced reconstruction
time. At the encoder, we leverage temporal correlation between frames and
deploy adaptive techniques based on compressive measurements from previous
frames. At the decoder, we exploit temporal correlation by using video frame
interpolation and temporal differential pulse code modulation. Simulations show
that our two proposed algorithms, VAL-VFI and VAL-IDA-VFI reconstruct higher
quality video, achieving state-of-the-art performance.
