By Topic

Visually Weighted Compressive Sensing: Measurement and Reconstruction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hyungkeuk Lee ; Wireless Network Lab., Yonsei Univ., Seoul, South Korea ; Heeseok Oh ; Sanghoon Lee ; Bovik, A.C.

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.

Published in:

Image Processing, IEEE Transactions on  (Volume:22 ,  Issue: 4 )