title: Visually weighted compressive sensing: measurement and reconstruction.
abstract: 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.
authors.affiliation: Wireless Network Laboratory, Yonsei University, Seoul, Korea. punktank@yonsei.ac.kr
authors.forename: Hyungkeuk
authors.initials: H
authors.lastname: Lee
authors.forename: Heeseok
authors.initials: H
authors.lastname: Oh
authors.forename: Sanghoon
authors.initials: S
authors.lastname: Lee
authors.forename: Alan Conrad
authors.initials: AC
authors.lastname: Bovik