RNN-based bitstream feature extraction method for codec classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wee, S. | - |
dc.contributor.author | Jeong, Je chang | - |
dc.date.accessioned | 2021-07-30T05:24:26Z | - |
dc.date.available | 2021-07-30T05:24:26Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4632 | - |
dc.description.abstract | In this paper, we propose codec classification algorithm based on recurrent neural network (RNN) model. In video compression, codecs, such as MPEG2 and H.264/AVC, have their own distinctive data structure. These unique structures which are almost shown in header can be considered their feature. The proposed algorithm exploits that characteristics for classifying unknown bitstreams into specific codec. According to the fact that RNN is appropriate to time series data for learning to classification/recognition, the feature of an encoded bitstream can be extracted. We constitute the encoded bitstream as an input and give the bitstream its label indicating codec index. Two standard codecs, MPEG2 and H.264/AVC, are used in experiment. Experimental results show that the proposed RNN model classified bitstreams into corresponding codecs to some extent. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPIE | - |
dc.title | RNN-based bitstream feature extraction method for codec classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jeong, Je chang | - |
dc.identifier.doi | 10.1117/12.2521425 | - |
dc.identifier.scopusid | 2-s2.0-85063880593 | - |
dc.identifier.bibliographicCitation | Proceedings of SPIE - The International Society for Optical Engineering, v.11049 | - |
dc.relation.isPartOf | Proceedings of SPIE - The International Society for Optical Engineering | - |
dc.citation.title | Proceedings of SPIE - The International Society for Optical Engineering | - |
dc.citation.volume | 11049 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Binary sequences | - |
dc.subject.keywordPlus | Data mining | - |
dc.subject.keywordPlus | Extraction | - |
dc.subject.keywordPlus | Feature extraction | - |
dc.subject.keywordPlus | Image compression | - |
dc.subject.keywordPlus | Motion Picture Experts Group standards | - |
dc.subject.keywordPlus | Recurrent neural networks | - |
dc.subject.keywordPlus | Bit stream | - |
dc.subject.keywordPlus | Bitstreams | - |
dc.subject.keywordPlus | Classification algorithm | - |
dc.subject.keywordPlus | Feature extraction methods | - |
dc.subject.keywordPlus | H.264/AVC | - |
dc.subject.keywordPlus | Recurrent neural network (RNN) | - |
dc.subject.keywordPlus | Time-series data | - |
dc.subject.keywordPlus | Classification (of information) | - |
dc.subject.keywordAuthor | bitstream feature extraction | - |
dc.subject.keywordAuthor | Classification | - |
dc.subject.keywordAuthor | recurrent neural network | - |
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.