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240411s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3385276
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Lee, Hongjae
|e verfasserin
|4 aut
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| 245 |
1 |
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|a RefQSR
|b Reference-Based Quantization for Image Super-Resolution Networks
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|c 2024
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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 16.04.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied. However, existing quantization methods developed for SISR have yet to effectively exploit image self-similarity, which is a new direction for exploration in this study. We introduce a novel method called reference-based quantization for image super-resolution (RefQSR) that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image. To this end, we design dedicated patch clustering and reference-based quantization modules and integrate them into existing SISR network quantization methods. The experimental results demonstrate the effectiveness of RefQSR on various SISR networks and quantization methods
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|a Journal Article
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|a Yoo, Jun-Sang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jung, Seung-Won
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 21., Seite 2823-2834
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:33
|g year:2024
|g day:21
|g pages:2823-2834
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|u http://dx.doi.org/10.1109/TIP.2024.3385276
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|d 33
|j 2024
|b 21
|h 2823-2834
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