Abstract
We address an important issue of fully low-cost and low-complexity video encoding for use in resource limited
sensors/devices. Conventional distributed video coding (DVC) does not actually meet this requirement because the
acquisition of video sequences still relies on the high-cost mechanism (sampling + compression). Recently, we have
proposed a distributed compressive video sensing (DCVS) framework to directly capture compressed video data called
measurements, while exploiting correlations among successive frames for video reconstruction at the decoder. The core
is to integrate the respective characteristics of DVC and compressive sensing (CS) to achieve CS-based single-pixel
camera-compatible video encoder. At DCVS decoder, video reconstruction can be formulated as a convex unconstrained
optimization problem via solving the sparse coefficients with respect to some basis functions. Nevertheless, the issue of
measurement rate allocation has not been considered yet in the literature. Actually, different measurement rates should
be adaptively assigned to different local regions by considering the sparsity of each region for improving reconstructed
quality. This paper investigates dynamic measurement rate allocation in block-based DCVS, which can adaptively adjust
measurement rates by estimating the sparsity of each block via feedback information. Simulation results have indicated
the effectiveness of our scheme. It is worth noting that our goal is to develop a novel fully low-complexity video
compression paradigm via the emerging compressive sensing and sparse representation technologies, and provide an
alternative scheme adaptive to the environment, where raw video data is not available, instead of competing compression
performances against the current compression standards (e.g., H.264/AVC) or DVC schemes which need raw data
available for encoding.
