  A deep image compression scheme is proposed in this paper, offering the
state-of-the-art compression efficiency, against the traditional JPEG,
JPEG2000, BPG and those popular learning based methodologies. This is achieved
by a novel conditional probably model with embedded priors which can accurately
approximate the entropy rate for rate-distortion optimization. It utilizes
three separable stacks to eliminate the blind spots in the receptive field for
better probability prediction and computation reduction. Those embedded priors
can be further used to help the image reconstruction when fused with latent
features, after passing through the proposed information compensation network
(ICN). Residual learning with generalized divisive normalization (GDN) based
activation is used in our encoder and decoder with fast convergence rate and
efficient performance. We have evaluated our model and other methods using
rate-distortion criteria, where distortion is measured by multi-scale
structural similarity (MS-SSIM). We have also discussed the impacts of various
distortion metrics on the reconstructed image quality. Besides, a field study
on perceptual quality is also given via a dedicated subjective assessment, to
compare the efficiency of our proposed methods and other conventional image
compression methods.
