• Corpus ID: 69370873

Context-adaptive Entropy Model for End-to-end Optimized Image Compression

@inproceedings{Lee2018ContextadaptiveEM,
  title={Context-adaptive Entropy Model for End-to-end Optimized Image Compression},
  author={Jooyoung Lee and Seunghyun Cho and Seunghwa Beack},
  booktitle={International Conference on Learning Representations},
  year={2018}
}
We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance… 

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