IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
A manifold lifting algorithm for multi-view compressive imaging
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Depth MAP compression VIA compressed sensing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Modified-CS: modifying compressive sensing for problems with partially known support
IEEE Transactions on Signal Processing
Compressive acquisition of dynamic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Block-Based Compressed Sensing of Images and Video
Foundations and Trends in Signal Processing
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Compressive Sensing (CS) allows the highly efficient acquisition of many signals that could be difficult to capture or encode using conventional methods. From a relatively small number of random measurements, a high-dimensional signal can be recovered if it has a sparse or near-sparse representation in a basis known to the decoder. In this paper, we consider the application of CS to video signals in order to lessen the sensing and compression burdens in single- and multi-camera imaging systems. In standard video compression, motion compensation and estimation techniques have led to improved sparse representations that are more easily compressible; we adapt these techniques for the problem of CS recovery. Using a coarse-to-fine reconstruction algorithm, we alternate between the tasks of motion estimation and motion-compensated wavelet-domain signal recovery. We demonstrate that our algorithm allows the recovery of video sequences from fewer measurements than either frame-by-frame or inter-frame difference recovery methods.