CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Cheeun | - |
dc.contributor.author | Baik, Sungyong | - |
dc.contributor.author | Kim, Heewon | - |
dc.contributor.author | Nah, Seungjun | - |
dc.contributor.author | Lee, Kyoung Mu | - |
dc.date.accessioned | 2022-12-20T06:06:57Z | - |
dc.date.available | 2022-12-20T06:06:57Z | - |
dc.date.created | 2022-12-07 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172930 | - |
dc.description.abstract | Despite breakthrough advances in image super-resolution (SR) with convolutional neural networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational complexity of SR networks. Quantization is one of the promising approaches to solve this problem. However, existing methods fail to quantize SR models with a bit-width lower than 8 bits, suffering from severe accuracy loss due to fixed bit-width quantization applied everywhere. In this work, to achieve high average bit-reduction with less accuracy loss, we propose a novel Content-Aware Dynamic Quantization (CADyQ) method for SR networks that allocates optimal bits to local regions and layers adaptively based on the local contents of an input image. To this end, a trainable bit selector module is introduced to determine the proper bit-width and quantization level for each layer and a given local image patch. This module is governed by the quantization sensitivity that is estimated by using both the average magnitude of image gradient of the patch and the standard deviation of the input feature of the layer. The proposed quantization pipeline has been tested on various SR networks and evaluated on several standard benchmarks extensively. Significant reduction in computational complexity and the elevated restoration accuracy clearly demonstrate the effectiveness of the proposed CADyQ framework for SR. Codes are available at https://github.com/Cheeun/CADyQ. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Baik, Sungyong | - |
dc.identifier.doi | 10.1007/978-3-031-20071-7_22 | - |
dc.identifier.scopusid | 2-s2.0-85142674932 | - |
dc.identifier.wosid | 000897035700022 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.13667 LNCS, pp.367 - 383 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 13667 LNCS | - |
dc.citation.startPage | 367 | - |
dc.citation.endPage | 383 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.subject.keywordPlus | Complex networks | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Image compression | - |
dc.subject.keywordPlus | Optical resolving power | - |
dc.subject.keywordPlus | Computational complexity | - |
dc.subject.keywordPlus | % reductions | - |
dc.subject.keywordPlus | Accuracy loss | - |
dc.subject.keywordPlus | Bit-Width | - |
dc.subject.keywordPlus | Content-aware | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Dynamic quantization | - |
dc.subject.keywordPlus | Image super resolutions | - |
dc.subject.keywordPlus | Quantisation | - |
dc.subject.keywordPlus | Superresolution | - |
dc.subject.keywordPlus | Ubiquitous application | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-031-20071-7_22 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.