  Deep learning has revolutionized many computer vision fields in the last few
years, including learning-based image compression. In this paper, we propose a
deep semantic segmentation-based layered image compression (DSSLIC) framework
in which the semantic segmentation map of the input image is obtained and
encoded as the base layer of the bit-stream. A compact representation of the
input image is also generated and encoded as the first enhancement layer. The
segmentation map and the compact version of the image are then employed to
obtain a coarse reconstruction of the image. The residual between the input and
the coarse reconstruction is additionally encoded as another enhancement layer.
Experimental results show that the proposed framework outperforms the
H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics across a
wide range of bit rates in RGB domain. Besides, since semantic segmentation map
is included in the bit-stream, the proposed scheme can facilitate many other
tasks such as image search and object-based adaptive image compression.
