Motion-Aware Decoding of Compressed-Sensed Video

Compressed sensing is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Perfect reconstruction may then be possible with much fewer than the Nyquist required number of data. In this paper, in particular, we consider a video system where acquisition is carried out in the form of direct compressive sampling (CS) with no other form of sophisticated encoding. Therefore, the burden of quality video sequence reconstruction falls solely on the receiver side. We show that effective implicit motion estimation and decoding can be carried out at the receiver or decoder side via sparsity-aware recovery. The receiver performs sliding-window interframe decoding that adaptively estimates Karhunen-Loève bases from adjacent previously reconstructed frames to enhance the sparse representation of each video frame block, such that the overall reconstruction quality is improved at any given fixed CS rate. Experimental results included in this paper illustrate the presented developments.