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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3263099
|2 doi
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|a pubmed24n1184.xml
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|a (DE-627)NLM355278375
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|a (NLM)37023144
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Fu, Haisheng
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Learned Image Compression With Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
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| 264 |
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|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 07.04.2023
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|a Date Revised 11.04.2023
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a 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 model of the latent representations and the encoding/decoding network architectures. Various models have been proposed, such as autoregressive, softmax, logistic mixture, Gaussian mixture, and Laplacian. Existing schemes only use one of these models. However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image. In this paper, we propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations, which can adapt to different contents in different images and different regions of one image more accurately and efficiently, given the same complexity. Besides, in the encoding/decoding network design part, we propose a concatenated residual blocks (CRB), where multiple residual blocks are serially connected with additional shortcut connections. The CRB can improve the learning ability of the network, which can further improve the compression performance. Experimental results using the Kodak, Tecnick-100 and Tecnick-40 datasets show that the proposed scheme outperforms all the leading learning-based methods and existing compression standards including VVC intra coding (4:4:4 and 4:2:0) in terms of the PSNR and MS-SSIM. The source code is available at https://github.com/fengyurenpingsheng
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4 |
|a Journal Article
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| 700 |
1 |
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|a Liang, Feng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lin, Jianping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Bing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Akbari, Mohammad
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liang, Jie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Guohe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Dong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tu, Chengjie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Jingning
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 17., Seite 2063-2076
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:32
|g year:2023
|g day:17
|g pages:2063-2076
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2023.3263099
|3 Volltext
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|a AR
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| 952 |
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|d 32
|j 2023
|b 17
|h 2063-2076
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