Scalable Structured Compressive Video Sampling with Hierarchical Subspace Learning

Conventional scalable compressive video sampling cannot sufficiently exploit the structured sparsity within video sequences. This paper proposes a novel quality scalable structured compressive video sampling (SS-CVS) framework with hierarchical subspace learning to support video transmission over heterogeneous network conditions. The proposed framework incorporates the union of data-driven subspaces (UoDS) model to introduce structured sparsity into sensing matrix for progressive acquisition of measurements. Hierarchical subspace learning is developed to generate the subspaces with their bases based on adaptive subspace clustering in a progressive fashion. Consequently, two hierarchical structures are derived to enable progressive mapping of subspaces and bases on the structured sensing matrices to support quality scalability. To guarantee the convergence of hierarchical subspace learning, the training set is updated with the adaptive group set from the reconstructed reference frames. It is demonstrated that SS-CVS can guarantee stable recovery for each quality layer under the constraints of block restricted isometric property (RIP). Furthermore, the proposed SS-CVS model is generalized to tensor subspaces for scalable compressive sampling of high-dimensional signals. Experimental results demonstrate that the proposed algorithm can achieve quality scalabilities with an improved reconstructed performance in comparison to the state-of-the-art scalable compressive video sampling methods.