Real-Time Adaptive Video Compression

Compressive sensing has been widely applied to problems in signal and imaging processing. In this work, we present an algorithm for predicting optimal real-time compression rates for video. The video data we consider is spatially compressed during the acquisition process, unlike in many of the standard methods. Rather than temporally compressing the frames at a fixed rate, our algorithm adaptively predicts the compression rate given the behavior of a few previous compressed frames. The algorithm uses polynomial fitting and simple filters, making it computationally feasible and easy to implement in hardware. Based on numerical simulations of real videos, the algorithm is able to capture object motion and approximate dynamics within the compressed frames. The adaptive video compression improves the quality of the reconstructed video (as compared to an equivalent fixed rate compression scheme) by several dB of peak signal-to-noise ratio without increasing the amount of information stored, as seen in numerical simulations presented here.