Learned Image Compression With Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules-Reference-Cited by-同舟云学术

Learned Image Compression With Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules

Published:2023 Issue: Volume:32 Page:2063-2076
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Fu Haisheng¹^ORCID,Liang Feng¹^ORCID,Lin Jianping²^ORCID,Li Bing¹^ORCID,Akbari Mohammad³^ORCID,Liang Jie³^ORCID,Zhang Guohe¹^ORCID,Liu Dong²^ORCID,Tu Chengjie⁴,Han Jingning⁵^ORCID

Affiliation:

1. School of Microelectronics, Xi’an Jiaotong University, Xi’an, China

2. CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, University of Science and Technology of China, Hefei, China

3. School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

4. Tencent Technologies, Shenzhen, China

5. Google Inc., Mountain View, CA, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shaanxi Province, China

Fundamental Research Funds for the Central Universities

Natural Sciences and Engineering Research Council of Canada

China Scholarship Council

Google Chrome University Research Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9991910/10091784.pdf?arnumber=10091784

Reference54 articles.

1. Learned Bi-Resolution Image Coding using Generalized Octave Convolutions

2. Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules

3. Transformer-based Image Compression

4. End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform

5. A Comprehensive Benchmark for Single Image Compression Artifact Reduction

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