Dynamic measurement rate allocation for distributed compressive video sensing

Hung Wei Chen*, Li Wei Kang, Chun Shien Lu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationVisual Communications and Image Processing 2010
Publication statusPublished - 2010
Externally publishedYes
EventVisual Communications and Image Processing 2010 - Huangshan, China
Duration: 2010 Jul 112010 Jul 14

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceVisual Communications and Image Processing 2010


  • Compressive sensing
  • Dictionary learning
  • Distributed compressive video sensing
  • Distributed video coding
  • Low-complexity video coding
  • Measurement rate allocation
  • Single-pixel camera
  • Sparse representation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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