Compressive Sensing for Multimedia Communications in Wireless Sensor Networks

Compressive Sensing is an emerging data acquisition scheme with the potential to reduce the number of measurements required by the Nyquist sampling theorem to acquire sparse signals. If a signal is sparse or compressible in a certain basis, compressive sensing computes inner products with a set of basis functions instead of uniformly sampling. In this paper, we examine the performance of compressive sensing for 2-D images in terms of complexity and quality of reconstruction, and consider the benefits of its application to imaging over Wireless Sensor Networks, where stringent constraints exist on energy and bandwidth consumption. We show that we can operate at very low data rates with reduced complexity and still achieve good image quality.