Distributed perceptual compressed sensing framework for multiview images

A perceptual-based compressed sensing (CS), which focuses the measurements and the recovery on the visually important low-frequency coefficients, is applied for multi-view image signals. High correlation among different views is exploited to generate signal prediction using disparity estimation and compensation techniques. A residual-based recovery is utilised as a joint recovery for the nonreference images to enhance the reconstruction performance. The proposed framework shows remarkable performance improvement over the conventional CS with joint recovery as well as the perceptualbased CS with independent recovery.