  We propose a deep learning system for attention-guided dual-layer image
compression (AGDL). In the AGDL compression system, an image is encoded into
two layers, a base layer and an attention-guided refinement layer. Unlike the
existing ROI image compression methods that spend an extra bit budget equally
on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and
near a saliency sketch within ROI that are critical to perceptual quality. Only
the critical pixels are further sampled by compressive sensing (CS) to form a
very compact refinement layer. Another novel CNN method is developed to jointly
decode the two compression layers for a much refined reconstruction, while
strictly satisfying the transmitted CS constraints on perceptually critical
pixels. Extensive experiments demonstrate that the proposed AGDL system
advances the state of the art in perception-aware image compression.
