Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods

CVPRW 2020 pp. 543-546

doi:10.1109/CVPRW50498.2020.00071 /cvprw/2020/cheng2020cvprw-low/

Abstract

In this paper, we provide a detailed description on our submitted method Kattolab to Workshop and Challenge on Learned Image Compression (CLIC) 2020. Our method mainly incorporates discretized Gaussian Mixture Likeli-hoods to previous state-of-the-art learned compression algorithms. Besides, we also describes the acceleration strategies and bit optimization with the low-rate constraint. Experimental results have demonstrated that our approach Kattolab achieves 0.9761 in terms of MS-SSIM at the rate constraint of 0.15 bpp during the validation phase.

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Cite

Text

Cheng et al. "Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00071

Markdown

[Cheng et al. "Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/cheng2020cvprw-low/) doi:10.1109/CVPRW50498.2020.00071

BibTeX

@inproceedings{cheng2020cvprw-low,
  title     = {{Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods}},
  author    = {Cheng, Zhengxue and Sun, Heming and Katto, Jiro},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {543-546},
  doi       = {10.1109/CVPRW50498.2020.00071},
  url       = {https://mlanthology.org/cvprw/2020/cheng2020cvprw-low/}
}