Learning Convolutional Networks for Content-Weighted Image Compression

@article{Li2017LearningCN,
  title={Learning Convolutional Networks for Content-Weighted Image Compression},
  author={Mu Li and Wangmeng Zuo and Shuhang Gu and Debin Zhao and Dongxiao Zhang},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2017},
  pages={3214-3223}
}
Lossy image compression is generally formulated as a joint rate-distortion optimization problem to learn encoder, quantizer, and decoder. Due to the non-differentiable quantizer and discrete entropy estimation, it is very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that: (i) the bit rate of the different parts of the image is adapted to local… CONTINUE READING

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