In recent deep image compression neural networks, the entropy model plays a
critical role in estimating the prior distribution of deep image encodings.
Existing methods combine hyperprior with local context in the entropy
estimation function. This greatly limits their performance due to the absence
of a global vision. In this work, we propose a novel Global Reference Model for
image compression to effectively leverage both the local and the global context
information, leading to an enhanced compression rate. The proposed method scans
decoded latents and then finds the most relevant latent to assist the
distribution estimating of the current latent. A by-product of this work is the
innovation of a mean-shifting GDN module that further improves the performance.
Experimental results demonstrate that the proposed model outperforms the
rate-distortion performance of most of the state-of-the-art methods in the
industry