Learned Image Compression with Large Capacity and Low Redundancy of Latent Representation-Reference-Cited by-同舟云学术

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Learned Image Compression with Large Capacity and Low Redundancy of Latent Representation

Published:2023-10-08 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Image Processing (ICIP)
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Author:

Meng Xiandong¹,Zhu Shuyuan²,Ma Siwei¹,Zeng Bing²

Affiliation:

1. Pengcheng Laboratory,Shenzhen,China

2. University of Electronic Science and Technology of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10221937/10221892/10222075.pdf?arnumber=10222075

Reference23 articles.

1. Joint autoregressive and hierarchical priors for learned image compression;minnen;Proc Adv Neural Inf Process Syst,2018

2. Variational image compression with a scale hyperprior;ballé;Proc Int Conf Learn Represent,2018

3. TESTIMAGES: a large-scale archive for testing visual devices and basic image processing algorithms;asuni;Proc Eurographics Italian Chapter Conf,2014

4. End-to-end optimized image compression with attention mechanism;zhou;Proc IEEE/CVF Conf Comput Vision Pattern Recognit Workshops,2019

5. A Robust Quality Enhancement Method Based on Joint Spatial-Temporal Priors for Video Coding

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multirate Progressive Entropy Model for Learned Image Compression;IEEE Transactions on Circuits and Systems for Video Technology;2024-08

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