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Visually Weighted Compressive Sensing: Measurement and Reconstruction

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4 Author(s)
Hyungkeuk Lee ; Wireless Network Laboratory, Yonsei University, Seoul, Korea ; Heeseok Oh ; Sanghoon Lee ; Alan Conrad Bovik

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:

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