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A deep image compression scheme is proposed in this paper, offering thestate-of-the-art compression efficiency, against the traditional JPEG,JPEG2000, BPG and those popular learning based methodologies. This is achievedby a novel conditional probably model with embedded priors which can accuratelyapproximate the entropy rate for rate-distortion optimization. It utilizesthree separable stacks to eliminate the blind spots in the receptive field forbetter probability prediction and computation reduction. Those embedded priorscan be further used to help the image reconstruction when fused with latentfeatures, after passing through the proposed information compensation network(ICN). Residual learning with generalized divisive normalization (GDN) basedactivation is used in our encoder and decoder with fast convergence rate andefficient performance. We have evaluated our model and other methods usingrate-distortion criteria, where distortion is measured by multi-scalestructural similarity (MS-SSIM). We have also discussed the impacts of variousdistortion metrics on the reconstructed image quality. Besides, a field studyon perceptual quality is also given via a dedicated subjective assessment, tocompare the efficiency of our proposed methods and other conventional imagecompression methods.

Gated Context Model with Embedded Priors for Deep Image Compression