Learning Accurate Entropy Model with Global Reference for Image Compression
This work addresses the need for more efficient image compression for applications like storage and transmission, though it appears incremental as it builds on existing hyperprior and local context methods.
The paper tackles the problem of limited performance in deep image compression due to the absence of global context in entropy models, proposing a Global Reference Model that leverages both local and global information to enhance compression rates, with experimental results showing it outperforms most state-of-the-art methods in rate-distortion performance.
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.