Block-based Compressed Sensing of Images and Video

J. E. Fowler, S. Mun, & E. W. Tramel

• Link
• PDF

Astract

Block-Based Compressed Sensing of Images and Video overviews the emerging concept of compressed sensing (CS) with a particular focus on recent proposals for its use with a variety of imaging media, including still images, motion video, as well as multiview images and video. Throughout, it considers a variety of CS reconstruction techniques proposed in recent literature and examines relative performance of several prominent reconstruction algorithms for each of the various imagery formats. Particular emphasis is placed on block-based measurement and reconstruction which has the advantages of significantly reduced memory and computation with respect to other approaches relying on full-frame CS measurement operators. Block-Based Compressed Sensing of Images and Video employs extensive experimental comparisons to evaluate various prominent reconstruction algorithms for still-image, motion-video, and multiview scenarios in terms of both reconstruction quality as well as computational complexity. It is not intended to serve as an indepth tutorial on the theory or mathematics of compressed sensing. The coverage of CS theory is brief, while the specifics of the application of block-based compressed sensing (BCS) to natural imagery consume the bulk of the discussion.

BibTeX

    article{fmt2012,
    Author = {James E. Fowler and Sungkwang Mun and Eric W. Tramel},
    Journal = {Foundations and Trends in Signal Processing},
    Month = {March},
    Number = {4},
    Pages = {297--416},
    Title = {Block-based Compressed Sensing of Images and Video},
    Volume = {4},
    Year = {2012}}