Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods

Zhengxue Cheng, Heming Sun, Jiro Katto

Research output: Contribution to journalArticlepeer-review


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 Likelihoods 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 and 0.9802 in terms of MS-SSIM at the rate constraint of 0.15 bpp during the validation phase and test phase, respectively.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2020 Apr 8

ASJC Scopus subject areas

  • General

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