  In this paper, we propose a scalable image compression scheme, including the
base layer for feature representation and enhancement layer for texture
representation. More specifically, the base layer is designed as the deep
learning feature for analysis purpose, and it can also be converted to the fine
structure with deep feature reconstruction. The enhancement layer, which serves
to compress the residuals between the input image and the signals generated
from the base layer, aims to faithfully reconstruct the input texture. The
proposed scheme can feasibly inherit the advantages of both
compress-then-analyze and analyze-then-compress schemes in surveillance
applications. The performance of this framework is validated with facial
images, and the conducted experiments provide useful evidences to show that the
proposed framework can achieve better rate-accuracy and rate-distortion
performance over conventional image compression schemes.
