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