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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3136608
|2 doi
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|a eng
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|a Niu, Yi
|e verfasserin
|4 aut
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|a NL-CALIC Soft Decoding Using Strict Constrained Wide-Activated Recurrent Residual Network
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 26.01.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this work, we propose a normalized Tanh activate strategy and a lightweight wide-activate recurrent structure to solve three key challenges of the soft-decoding of near-lossless codes: 1. How to add an effective strict constrained peak absolute error (PAE) boundary to the network; 2. An end-to-end solution that is suitable for different quantization steps (compression ratios). 3. Simple structure that favors the GPU and FPGA implementation. To this end, we propose a Wide-activated Recurrent structure with a normalized Tanh activate strategy for Soft-Decoding (WRSD). Experiments demonstrate the effectiveness of the proposed WRSD technique that WRSD outperforms better than the state-of-the-art soft decoders with less than 5% number of parameters, and every computation node of WRSD requires less than 64KB storage for the parameters which can be easily cached by most of the current consumer-level GPUs. Source code is available at https://github.com/dota-109/WRSD
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|a Journal Article
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|a Liu, Chang
|e verfasserin
|4 aut
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|a Ma, Mingming
|e verfasserin
|4 aut
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| 700 |
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|a Li, Fu
|e verfasserin
|4 aut
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| 700 |
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|a Chen, Zhiwen
|e verfasserin
|4 aut
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| 700 |
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|a Shi, Guangming
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 01., Seite 1243-1257
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
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|g volume:31
|g year:2022
|g day:01
|g pages:1243-1257
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|u http://dx.doi.org/10.1109/TIP.2021.3136608
|3 Volltext
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