Subspace Learning with Structured Sparsity for Compressive Video Sampling
Existing sparse representation with subspace learning is hampered by the intersection of subspaces of bases. With structured sparsity to enable the prior knowledge of signal statistics, this paper proposes a novel compressive video sampling by subspace learning to minimize the intersection of subspaces. As the measurement, the block coherence is optimized with the regularized learning to generate a class of independent bases associated with the subspaces. Thus, the proposed framework can make a compact block sparse representation based on the derived basis in an efficient and adaptive manner. The block-based recovery of video sequences is demonstrated to be stable under the constraint of block restricted isometric property (RIP). Experimental results show that the proposed method outperforms existing compressive video sampling schemes.