Visually weighted compressive sensing: Measurement and reconstruction

Hyungkeuk Lee, Heeseok Oh, Sanghoon Lee, Alan Conrad Bovik

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Compressive sensing (CS) makes it possible to more naturally create compact representations of data with respect to a desired data rate. Through wavelet decomposition, smooth and piecewise smooth signals can be represented as sparse and compressible coefficients. These coefficients can then be effectively compressed via the CS. Since a wavelet transform divides image information into layered blockwise wavelet coefficients over spatial and frequency domains, visual improvement can be attained by an appropriate perceptually weighted CS scheme. We introduce such a method in this paper and compare it with the conventional CS. The resulting visual CS model is shown to deliver improved visual reconstructions.

Original languageEnglish
Article number6374249
Pages (from-to)1444-1455
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number4
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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