Low bitrate image compression with discretized gaussian mixture likelihoods

Zhengxue Cheng, Heming Sun, Jiro Katto

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
出版社IEEE Computer Society
ページ543-546
ページ数4
ISBN(電子版)9781728193601
DOI
出版ステータスPublished - 2020 6
イベント2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, United States
継続期間: 2020 6 142020 6 19

出版物シリーズ

名前IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2020-June
ISSN(印刷版)2160-7508
ISSN(電子版)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
CountryUnited States
CityVirtual, Online
Period20/6/1420/6/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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