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

Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key components of learned image compression are the entropy...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 32; S. 1
Hauptverfasser:	Fu, Haisheng, Liang, Feng, Lin, Jianping, Li, Bing, Akbari, Mohammad, Liang, Jie, Zhang, Guohe, Liu, Dong, Tu, Chengjie, Han, Jingning
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Coding Complexity theory Computer architecture Context modeling Correlation Decoding Deep learning Deep learning-based image compression Encoding-Decoding Entropy Entropy coding Gaussian Mixture Model Image coding Image compression Network design Probabilistic models Representations Residual Network Source code
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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