Compressive video sampling from a union of data-driven subspaces
Recently, compressive sampling (CS) is an active research field of signal processing. To further decrease the necessary measurements and get more efficient recovery of a signal x, recent approaches assume that x lives in a union of subspaces (UoS). Unlike previous approaches, this paper proposes a novel method to sample and recover an unknown signal from a union of data-driven subspaces (UoDS). Instead of a fix set of supports, this UoDS is learned from classified signal series which are uniquely formed by block matching. The basis of these data-driven subspaces is regularized after dimensionality reduction by principal component extraction. A corresponding recovery solution with provable performance guarantees is also given, which takes full advantage of block-sparsity structure and improves the recovery efficiency. In practice, the proposed scheme is fulfilled to sample and recover frames in video sequences. The experimental results demonstrate that the proposed video sampling behaves better performance in sampling and recovery than the classical CS.