MLIC: Multi-Reference Entropy Model for Learned Image Compression-Reference-Cited by-同舟云学术

MLIC: Multi-Reference Entropy Model for Learned Image Compression

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
language:
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Author:

Jiang Wei¹^ORCID,Yang Jiayu²^ORCID,Zhai Yongqi²^ORCID,Ning Peirong²^ORCID,Gao Feng¹^ORCID,Wang Ronggang³^ORCID

Affiliation:

1. Peking University, Shenzhen, China

2. Peking University & Peng Cheng Laboratory, Shenzhen, China

3. Peking University, Peng Cheng Laboratory, & Migu Culture Technology Co., Ltd, Shenzhen, China

Funder

Shenzhen Science and Technology Program-Shenzhen Hong Kong joint funding project

National Natural Science Foundation of China

Shenzhen Science and Technology Program-Shenzhen Cultivation of Excellent Scientific and Technological Innovation Talents project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611694

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3. Johannes Ballé , Valero Laparra , and Eero P Simoncelli . 2016 . Density modeling of images using a generalized normalization transformation . In International Conference on Learning Representations. Johannes Ballé, Valero Laparra, and Eero P Simoncelli. 2016. Density modeling of images using a generalized normalization transformation. In International Conference on Learning Representations.

4. Johannes Ballé , Valero Laparra , and Eero P Simoncelli . 2017 . End-to-end optimized image compression . In International Conference on Learning Representations. Johannes Ballé, Valero Laparra, and Eero P Simoncelli. 2017. End-to-end optimized image compression. In International Conference on Learning Representations.

5. Johannes Ballé , David Minnen , Saurabh Singh , Sung Jin Hwang , and Nick Johnston . 2018 . Variational image compression with a scale hyperprior . In International Conference on Learning Representations. Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. 2018. Variational image compression with a scale hyperprior. In International Conference on Learning Representations.

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