Foveated Compressed Sensing

Abstract

Combining the principles behind Compressed Sensing theory with the possibility of implementing variable spatial resolution by means of an operator inspired by the human visual system may yield significant compression performances on both 1D and 2D signals. The solution provides spatially variable quality of the reconstructed information, enabling better approximation of specific regions of interest. Two distinct algorithms are compared in terms of reconstruction error and compression ratio on a set of ECG records and natural images.