Wireless Deep Video Semantic Transmission

In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE journal on selected areas in communications Ročník 41; číslo 1; s. 214 - 229
Hlavní autoři:	Wang, Sixian, Dai, Jincheng, Liang, Zijian, Niu, Kai, Si, Zhongwei, Dong, Chao, Qin, Xiaoqi, Zhang, Ping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Channels Coding Computer architecture Design optimization Domains Encoding Entropy Feature extraction Frames (data processing) joint source-channel coding Machine vision nonlinear transform Performance measurement rate-distortion Semantic communications Semantics Task analysis Transforms Transmission rate (communications) Video communication Video transmission Vision systems Wireless communication Wireless sensor networks
ISSN:	0733-8716, 1558-0008
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.
AbstractList	In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.
Author	Wang, Sixian Dong, Chao Liang, Zijian Si, Zhongwei Niu, Kai Dai, Jincheng Zhang, Ping Qin, Xiaoqi
Author_xml	– sequence: 1 givenname: Sixian orcidid: 0000-0002-0621-1285 surname: Wang fullname: Wang, Sixian organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 2 givenname: Jincheng orcidid: 0000-0002-0310-568X surname: Dai fullname: Dai, Jincheng email: daijincheng@bupt.edu.cn organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 3 givenname: Zijian surname: Liang fullname: Liang, Zijian organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 4 givenname: Kai orcidid: 0000-0002-8076-1867 surname: Niu fullname: Niu, Kai organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 5 givenname: Zhongwei orcidid: 0000-0002-8286-2872 surname: Si fullname: Si, Zhongwei organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 6 givenname: Chao orcidid: 0000-0002-4922-7762 surname: Dong fullname: Dong, Chao organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 7 givenname: Xiaoqi orcidid: 0000-0002-5788-0657 surname: Qin fullname: Qin, Xiaoqi organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 8 givenname: Ping orcidid: 0000-0002-0269-104X surname: Zhang fullname: Zhang, Ping organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
BookMark	eNp9kE1LAzEQhoNUsFV_gHhZ8ORhazJJmuyx1G8KHlr1GLKbCaS0uzXZHvz3prR48OBhGAbeZ2Z4RmTQdi0ScsXomDFa3b0uprMxUIAxB2CVUidkyKTUJaVUD8iQKs5LrdjkjIxSWlHKhNAwJLefIeIaUyruEbfFR3DYFQvc2LYPTbGMtk2bkFLo2gty6u064eWxn5P3x4fl7Lmcvz29zKbzsoGK96VzCNRZXaMQwqPiLJerARqvKOQJpKwVr13tGy24BNTMTSRI7xkV2vFzcnPYu43d1w5Tb1bdLrb5pAElRSWVAp5T6pBqYpdSRG-a0Ns-_9lHG9aGUbP3YvZezN6LOXrJJPtDbmPY2Pj9L3N9YAIi_uarSvIM8B_4aW6E
CODEN	ISACEM
CitedBy_id	crossref_primary_10_1016_j_phycom_2025_102763 crossref_primary_10_1109_TWC_2024_3519325 crossref_primary_10_1109_TWC_2024_3520870 crossref_primary_10_1109_TWC_2024_3512652 crossref_primary_10_1109_JSAC_2023_3288236 crossref_primary_10_3390_s25092823 crossref_primary_10_1109_JIOT_2024_3448538 crossref_primary_10_1109_TCOMM_2024_3386577 crossref_primary_10_1109_TGCN_2023_3321113 crossref_primary_10_1109_TCCN_2024_3435524 crossref_primary_10_1109_TCCN_2023_3306852 crossref_primary_10_1109_TNSE_2025_3566336 crossref_primary_10_1109_LCOMM_2024_3452715 crossref_primary_10_1109_JSTSP_2023_3304140 crossref_primary_10_1109_TCOMM_2024_3450877 crossref_primary_10_1016_j_jnca_2025_104181 crossref_primary_10_1109_MWC_010_2300180 crossref_primary_10_1109_TIFS_2025_3573192 crossref_primary_10_1109_JSAC_2025_3559160 crossref_primary_10_1109_LWC_2024_3488859 crossref_primary_10_1109_TCOMM_2024_3385915 crossref_primary_10_1109_JIOT_2024_3378779 crossref_primary_10_1109_TWC_2024_3370497 crossref_primary_10_1109_JSAC_2023_3288246 crossref_primary_10_1109_TNSM_2025_3563257 crossref_primary_10_1631_FITEE_2300196 crossref_primary_10_1109_JSAC_2023_3288249 crossref_primary_10_1109_LCOMM_2024_3389709 crossref_primary_10_1109_TCOMM_2024_3364990 crossref_primary_10_1109_JLT_2024_3485065 crossref_primary_10_1109_TWC_2025_3534995 crossref_primary_10_3390_electronics14051005 crossref_primary_10_1109_JIOT_2025_3555437 crossref_primary_10_1109_JIOT_2025_3529492 crossref_primary_10_1109_JSAC_2025_3559158 crossref_primary_10_1109_JSAC_2025_3559115 crossref_primary_10_1109_MCOM_004_2300228 crossref_primary_10_3390_electronics14050956 crossref_primary_10_1109_TCCN_2024_3522140 crossref_primary_10_1016_j_dcan_2025_06_010 crossref_primary_10_1109_ACCESS_2023_3319083 crossref_primary_10_1109_TWC_2024_3452481 crossref_primary_10_1109_JIOT_2024_3352737 crossref_primary_10_1109_JIOT_2025_3532484 crossref_primary_10_1109_TWC_2024_3409735 crossref_primary_10_1109_COMST_2023_3333342 crossref_primary_10_1109_JSAC_2025_3536557 crossref_primary_10_1016_j_dcan_2024_12_001 crossref_primary_10_1109_JSAC_2025_3531537 crossref_primary_10_1109_TCOMM_2024_3369697 crossref_primary_10_1109_JIOT_2025_3581778 crossref_primary_10_3390_s24030957 crossref_primary_10_1109_JIOT_2024_3367438 crossref_primary_10_1109_TVT_2024_3456099 crossref_primary_10_1109_TCOMM_2024_3480982 crossref_primary_10_1109_JSAC_2025_3531579 crossref_primary_10_1109_JIOT_2025_3553504 crossref_primary_10_1109_TMC_2023_3349315 crossref_primary_10_1109_MWC_001_2300014 crossref_primary_10_1109_ACCESS_2024_3399174 crossref_primary_10_1109_TCCN_2024_3520132 crossref_primary_10_3390_electronics14081666 crossref_primary_10_1109_JSAC_2025_3559149 crossref_primary_10_1109_TCOMM_2024_3447870 crossref_primary_10_1109_MWC_001_2400204 crossref_primary_10_1109_TWC_2024_3472612 crossref_primary_10_3390_electronics12132755 crossref_primary_10_1109_ACCESS_2025_3584000 crossref_primary_10_1109_JIOT_2024_3464614 crossref_primary_10_3390_app15137227 crossref_primary_10_1109_JSAC_2025_3559140 crossref_primary_10_1109_JPROC_2024_3520707 crossref_primary_10_1109_JIOT_2025_3545667 crossref_primary_10_1109_TCOMM_2024_3471992 crossref_primary_10_1109_COMST_2023_3300664 crossref_primary_10_1007_s11432_024_4337_1 crossref_primary_10_1109_JIOT_2023_3305011 crossref_primary_10_1109_COMST_2024_3416309 crossref_primary_10_32604_cmes_2023_046837 crossref_primary_10_1109_LCOMM_2023_3339776 crossref_primary_10_1109_JSTSP_2024_3405853 crossref_primary_10_1109_TIFS_2025_3573212 crossref_primary_10_1109_JSAC_2025_3559134 crossref_primary_10_1109_TBC_2025_3559003 crossref_primary_10_1016_j_jfranklin_2025_107598 crossref_primary_10_1109_TWC_2024_3450697
Cites_doi	10.1109/MWC.017.2100705 10.1007/s11263-018-01144-2 10.1109/49.947033 10.1109/JSAC.2022.3180802 10.1109/CVPR.2019.01126 10.1109/JSAIT.2020.2987203 10.1109/CVPR.2017.291 10.1109/CVPR46437.2021.00405 10.1109/TWC.2021.3090048 10.1109/TIP.2020.3016485 10.1109/ICASSP.2018.8461983 10.1109/MCOM.2018.1700839 10.1109/JSAC.2020.3036955 10.1016/j.patrec.2008.04.005 10.1002/j.1538-7305.1948.tb01338.x 10.1007/978-3-540-88682-2_5 10.1109/JSAC.2022.3191354 10.1109/TCSVT.2003.815165 10.1109/MCOM.001.2200099 10.1109/ICCV48922.2021.00986 10.1109/MSP.2010.938080 10.1016/j.eng.2021.11.003 10.1109/TPAMI.2020.2988453 10.1109/TCOMM.2018.2814603 10.1109/PCS.2018.8456272 10.1109/TIT.1981.1056282 10.1109/JSTSP.2020.3034501 10.1109/TSP.2021.3071210 10.1109/TCSVT.2012.2221191 10.1155/2007/47517 10.1109/TCCN.2019.2919300
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD L7M
DOI	10.1109/JSAC.2022.3221977
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-0008
EndPage	229
ExternalDocumentID	10_1109_JSAC_2022_3221977 9953110
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 92067202; 62001049; 62071058; 61971062 funderid: 10.13039/501100001809 – fundername: Beijing Natural Science Foundation grantid: 4222012 funderid: 10.13039/501100004826
GroupedDBID	-~X .DC 0R~ 29I 3EH 4.4 41~ 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ADRHT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 IBMZZ ICLAB IES IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TN5 VH1 AAYXX CITATION 7SP 8FD L7M
ID	FETCH-LOGICAL-c293t-dde20da8be444fe731e73db22cf70231e255b73bdbfc84352e81d6525ff1048d3
IEDL.DBID	RIE
ISICitedReferencesCount	128
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000927934500014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0733-8716
IngestDate	Sun Nov 30 05:03:38 EST 2025 Sat Nov 29 03:23:04 EST 2025 Tue Nov 18 22:07:53 EST 2025 Wed Aug 27 02:15:09 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c293t-dde20da8be444fe731e73db22cf70231e255b73bdbfc84352e81d6525ff1048d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4922-7762 0000-0002-8076-1867 0000-0002-8286-2872 0000-0002-0621-1285 0000-0002-5788-0657 0000-0002-0269-104X 0000-0002-0310-568X
PQID	2754957723
PQPubID	85481
PageCount	16
ParticipantIDs	crossref_citationtrail_10_1109_JSAC_2022_3221977 proquest_journals_2754957723 crossref_primary_10_1109_JSAC_2022_3221977 ieee_primary_9953110
PublicationCentury	2000
PublicationDate	2023-Jan. 2023-1-00 20230101
PublicationDateYYYYMMDD	2023-01-01
PublicationDate_xml	– month: 01 year: 2023 text: 2023-Jan.
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE journal on selected areas in communications
PublicationTitleAbbrev	J-SAC
PublicationYear	2023
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref15 ref14 ref11 ref10 mentzer (ref31) 2020; 33 ref17 ref19 choi (ref16) 2019 ref18 krizhevsky (ref20) 2009 ballé (ref32) 2016 reed (ref49) 2015 ref46 ref48 ref47 ref42 ref44 zhang (ref4) 2022; 8 ref8 wang (ref39) 2004; 2 ref9 ref5 minnen (ref24) 2018; 31 ballé (ref21) 2017 wiegand (ref41) 2003; 13 li (ref27) 2021; 34 ref35 ref34 ref36 ref30 jaderberg (ref33) 2015; 28 bossen (ref37) 2013; 12 ref2 ref1 ref38 kingma (ref40) 2014 ref26 hoydis (ref43) 2022 ref25 bjontegaard (ref45) 2001 ref22 qin (ref6) 2021 ref28 ref29 ballé (ref23) 2018 ballé (ref3) 2018 seo (ref7) 2021
References_xml	– ident: ref8 doi: 10.1109/MWC.017.2100705 – volume: 12 year: 2013 ident: ref37 publication-title: Common Test Conditions and Software Reference Configurations – ident: ref36 doi: 10.1007/s11263-018-01144-2 – year: 2001 ident: ref45 publication-title: Calculation of Average PSNR Differences Between RD-Curves – ident: ref12 doi: 10.1109/49.947033 – ident: ref9 doi: 10.1109/JSAC.2022.3180802 – ident: ref29 doi: 10.1109/CVPR.2019.01126 – ident: ref28 doi: 10.1109/JSAIT.2020.2987203 – start-page: 1 year: 2015 ident: ref49 article-title: Training deep neural networks on noisy labels with bootstrapping publication-title: Proc Int Conf Learn Represent (Workshop) – ident: ref30 doi: 10.1109/CVPR.2017.291 – start-page: 1 year: 2017 ident: ref21 article-title: End-to-end optimized image compression publication-title: Proc Int Conf Learn Represent – ident: ref46 doi: 10.1109/CVPR46437.2021.00405 – year: 2014 ident: ref40 article-title: Adam: A method for stochastic optimization publication-title: arXiv 1412 6980 – ident: ref18 doi: 10.1109/TWC.2021.3090048 – ident: ref34 doi: 10.1109/TIP.2020.3016485 – ident: ref15 doi: 10.1109/ICASSP.2018.8461983 – ident: ref26 doi: 10.1109/MCOM.2018.1700839 – ident: ref19 doi: 10.1109/JSAC.2020.3036955 – ident: ref48 doi: 10.1016/j.patrec.2008.04.005 – ident: ref1 doi: 10.1002/j.1538-7305.1948.tb01338.x – ident: ref47 doi: 10.1007/978-3-540-88682-2_5 – ident: ref38 doi: 10.1109/JSAC.2022.3191354 – start-page: 1 year: 2016 ident: ref32 article-title: Density modeling of images using a generalized normalization transformation publication-title: Proc Int Conf Learn Represent – volume: 34 start-page: 1 year: 2021 ident: ref27 article-title: Deep contextual video compression publication-title: Proc Adv Neural Inf Process Syst – volume: 13 start-page: 560 year: 2003 ident: ref41 article-title: overview of the h.264/avc video coding standard publication-title: IEEE Transactions on Circuits and Systems for Video Technology doi: 10.1109/TCSVT.2003.815165 – volume: 2 start-page: 1398 year: 2004 ident: ref39 article-title: Multiscale structural similarity for image quality assessment publication-title: Proc 37th Asilomar Conf Signals Syst Comput – year: 2022 ident: ref43 article-title: Sionna: An open-source library for next-generation physical layer research publication-title: arXiv 2203 11854 – ident: ref10 doi: 10.1109/MCOM.001.2200099 – volume: 33 start-page: 11913 year: 2020 ident: ref31 article-title: High-fidelity generative image compression publication-title: Proc Adv Neural Inf Process Syst – start-page: 1 year: 2018 ident: ref23 article-title: Variational image compression with a scale hyperprior publication-title: Proc Int Conf Learn Represent – volume: 28 year: 2015 ident: ref33 article-title: Spatial transformer networks publication-title: Advances in neural information processing systems – ident: ref35 doi: 10.1109/ICCV48922.2021.00986 – year: 2021 ident: ref6 article-title: Semantic communications: Principles and challenges publication-title: arXiv 2201 01389 – ident: ref11 doi: 10.1109/MSP.2010.938080 – volume: 8 start-page: 60 year: 2022 ident: ref4 article-title: Toward wisdom-evolutionary and primitive-concise 6G: A new paradigm of semantic communication networks publication-title: Engineering doi: 10.1016/j.eng.2021.11.003 – ident: ref44 doi: 10.1109/TPAMI.2020.2988453 – ident: ref14 doi: 10.1109/TCOMM.2018.2814603 – ident: ref22 doi: 10.1109/PCS.2018.8456272 – ident: ref2 doi: 10.1109/TIT.1981.1056282 – volume: 31 start-page: 1 year: 2018 ident: ref24 article-title: Joint autoregressive and hierarchical priors for learned image compression publication-title: Proc Adv Neural Inf Process Syst – ident: ref25 doi: 10.1109/JSTSP.2020.3034501 – ident: ref5 doi: 10.1109/TSP.2021.3071210 – start-page: 1182 year: 2019 ident: ref16 article-title: Neural joint source-channel coding publication-title: Proc 36th Int Conf Mach Learn – ident: ref42 doi: 10.1109/TCSVT.2012.2221191 – year: 2021 ident: ref7 article-title: Semantics-native communication with contextual reasoning publication-title: arXiv 2108 05681 – year: 2009 ident: ref20 article-title: Learning multiple layers of features from tiny images – ident: ref13 doi: 10.1155/2007/47517 – ident: ref17 doi: 10.1109/TCCN.2019.2919300 – start-page: 1 year: 2018 ident: ref3 article-title: Integer networks for data compression with latent-variable models publication-title: Proc Int Conf Learn Represent
SSID	ssj0014482
Score	2.7078137
Snippet	In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	214
SubjectTerms	Channels Coding Computer architecture Design optimization Domains Encoding Entropy Feature extraction Frames (data processing) joint source-channel coding Machine vision nonlinear transform Performance measurement rate-distortion Semantic communications Semantics Task analysis Transforms Transmission rate (communications) Video communication Video transmission Vision systems Wireless communication Wireless sensor networks
Title	Wireless Deep Video Semantic Transmission
URI	https://ieeexplore.ieee.org/document/9953110 https://www.proquest.com/docview/2754957723
Volume	41
WOSCitedRecordID	wos000927934500014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0008 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014482 issn: 0733-8716 databaseCode: RIE dateStart: 19830101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB5q8aAHX1WsVsnBi2LaZLPbTY6lWsRDEarSW2h2Z6HQF334-53dpEFRBA-BQHZD-HYyM9_OzgzADTci1DwTPhknIihCh36iFPdjFRgei5GtGe6aTch-Px4Ok5cK3Je5MIjoDp9h0966WL6eq43dKmslCUmMzafakbKd52qVEQOiGS5iIKPItySgiGCGQdJ6HnS6xAQZa5L0huTwfLNBrqnKD03szEvv8H8fdgQHhRvpdfJ1P4YKzk5g_0txwRrc2oOtE1Jk3gPiwnsfa5x7A5wSlGPlORtFa2w3y07hrff42n3yi8YIviLrvPZJJbFAj-IMOecGZRTSpTPGlJG2nhsST8hklOnMqJj8IYbklbYFE8YQ-4p1dAbV2XyG5-AZMufkIoWKJIqz0MT0nF6bGIEYKcbrEGyhSlVRNdw2r5ikjj0ESWrRTS26aYFuHe7KKYu8ZMZfg2sWznJggWQdGtv1SIufapUySWRWEB2ILn6fdQl7tht8vkPSgOp6ucEr2FUf6_Fqee3k5RO79rqx
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED-GCuqDX1OcTu2DL4rd2jSx7eOYjqlzCJuyt7ImFxjMbezDv99L1hVFEXwoFJqU8sv17n653B3AJdfCVzwVLhknIihC-W4sJXcj6Wkeib6pGW6bTYTtdtTrxS8FuMlzYRDRHj7Dirm1sXw1lguzVVaNY5IYk0-1bjpnZdlaecyAiIaNGYRB4BoakMUwfS-uPnZqdeKCjFVIfn1yeb5ZIdtW5Ycutgamsfu_T9uDncyRdGrLld-HAo4OYPtLecEiXJmjrUNSZc4d4sR5GygcOx18JzAH0rFWilbZbJcdwmvjvltvullrBFeSfZ67pJSYp_pRipxzjWHg06VSxqQOTUU3JKaQhkGqUi0j8ogYkl96K5jQmvhXpIIjWBuNR3gMjiaDTk6SL0mmOPN1RM_ptbEWiIFkvATeCqpEZnXDTfuKYWL5gxcnBt3EoJtk6JbgOp8yWRbN-Gtw0cCZD8yQLEF5tR5J9lvNEhYSnRVECIKT32ddwGaz-9xKWg_tp1PYMr3hl_slZVibTxd4BhvyYz6YTc-t7HwCpgG9-g
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Wireless+Deep+Video+Semantic+Transmission&rft.jtitle=IEEE+journal+on+selected+areas+in+communications&rft.au=Wang%2C+Sixian&rft.au=Dai%2C+Jincheng&rft.au=Liang%2C+Zijian&rft.au=Niu%2C+Kai&rft.date=2023-01-01&rft.pub=IEEE&rft.issn=0733-8716&rft.volume=41&rft.issue=1&rft.spage=214&rft.epage=229&rft_id=info:doi/10.1109%2FJSAC.2022.3221977&rft.externalDocID=9953110
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0733-8716&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0733-8716&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0733-8716&client=summon