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The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. In this paper, we focus on how to improve the sampling efficiency for CS-based image compression by using our proposed adaptive sampling mechanism on the block-based CS (BCS), especially the reweighted one. To achieve this goal, two solutions...
The compressed sensing (CS) theory shows that a sparse signal can be recovered at a sampling rate that is (much) lower than the required Nyquist rate. In practice, many image signals are sparse in a certain domain, and because of this, the CS theory has been successfully applied to the image compression in the past few years. The most popular CS-based image compression scheme is the block-based CS...
According to the compressed sensing (CS) theory, a signal that is sparse in a certain domain can be nearly exactly recovered from a few measurements where the sampling rate is lower than the Nyquist rate. This theory has been successfully applied to the image compression in the past few years as most image signals are highly sparse. In this paper, we apply an adaptive sampling mechanism to the reweighted...
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