Lossless Coding of Multimodal Image Pairs Based on Image-To-Image Translation

Multimodal image coding often uses standard encoding algorithms, which do not exploit multimodality characteristics. This paper proposes a new cross-modality prediction approach for lossless coding of multimodal images, based on a Generative Adversarial Network (GAN). The GAN is added to the predict...

Full description

Saved in:
Bibliographic Details
Published in:European Workshop on Visual Information Processing pp. 1 - 6
Main Authors: Parracho, Joao O., Thomaz, Lucas A., Tavora, Luis M. N., Assuncao, Pedro A. A., Faria, Sergio M. M.
Format: Conference Proceeding
Language:English
Published: IEEE 11.09.2022
Subjects:
Computed tomography
Generative adversarial networks
Generative predictive coding
Image coding
Information processing
Learning based prediction
Lossless image coding
Multimodal image coding
Prediction algorithms
Versatile Video Coding
Video coding
Visualization
ISSN:2471-8963
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Multimodal image coding often uses standard encoding algorithms, which do not exploit multimodality characteristics. This paper proposes a new cross-modality prediction approach for lossless coding of multimodal images, based on a Generative Adversarial Network (GAN). The GAN is added to the prediction loop of the Versatile Video Coding (VVC) lossless encoder to perform cross-modality translation of an image to its counterpart modality. Then, such synthesized image is used as reference for inter prediction, followed by further optimization that includes rescaling and brightness adjustment. A publicly available dataset of Positron Emission Tomography (PET) and Computed Tomography (CT) image pairs is used to assess the performance of the proposed multimodal lossless image coding framework. In comparison with single modality coding using the VVC standard, average coding gains of 6.83% are achieved for the inter-coded PET images.
AbstractList Multimodal image coding often uses standard encoding algorithms, which do not exploit multimodality characteristics. This paper proposes a new cross-modality prediction approach for lossless coding of multimodal images, based on a Generative Adversarial Network (GAN). The GAN is added to the prediction loop of the Versatile Video Coding (VVC) lossless encoder to perform cross-modality translation of an image to its counterpart modality. Then, such synthesized image is used as reference for inter prediction, followed by further optimization that includes rescaling and brightness adjustment. A publicly available dataset of Positron Emission Tomography (PET) and Computed Tomography (CT) image pairs is used to assess the performance of the proposed multimodal lossless image coding framework. In comparison with single modality coding using the VVC standard, average coding gains of 6.83% are achieved for the inter-coded PET images.
Author Parracho, Joao O.
Assuncao, Pedro A. A.
Thomaz, Lucas A.
Faria, Sergio M. M.
Tavora, Luis M. N.
Author_xml – sequence: 1
  givenname: Joao O.
  surname: Parracho
  fullname: Parracho, Joao O.
  email: jparracho@co.it.pt
  organization: Instituto de Telecomunicações,Leiria,Portugal
– sequence: 2
  givenname: Lucas A.
  surname: Thomaz
  fullname: Thomaz, Lucas A.
  email: lucas.thomaz@co.it.pt
  organization: Instituto de Telecomunicações,Leiria,Portugal
– sequence: 3
  givenname: Luis M. N.
  surname: Tavora
  fullname: Tavora, Luis M. N.
  email: luis.tavora@co.it.pt
  organization: Instituto de Telecomunicações,Leiria,Portugal
– sequence: 4
  givenname: Pedro A. A.
  surname: Assuncao
  fullname: Assuncao, Pedro A. A.
  email: amado@co.it.pt
  organization: Instituto de Telecomunicações,Leiria,Portugal
– sequence: 5
  givenname: Sergio M. M.
  surname: Faria
  fullname: Faria, Sergio M. M.
  email: sergio.faria@co.it.pt
  organization: Instituto de Telecomunicações,Leiria,Portugal
BookMark eNotkL1OwzAURg0CiVL6BAz4BRJ87fhvhKhApFZ0aFkrJ76ujJIYxWXg7RnS6ZPOkc7w3ZObMY1IyBOwEoDZ5_Xhq9lJYY0tOeO8tJZzzdUVWVltQClZKcWFuCYLXmkojFXijqxy_maMgQJQrFqQ7Sbl3GPOtE4-jieaAt3-9uc4JO962gzuhHTn4pTpq8voaRpnWOxTMdv95Mbcu3NM4wO5Da7PuLrskhze1vv6o9h8vjf1y6aInIlz0QYTWiM6CWCtrDoPgXdSt1UHLYgQ0AeQzAVkHffSG221ZRJNcBI1VkEsyePcjYh4_Jni4Ka_4-UA8Q9MtFIg
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/EUVIP53989.2022.9922726
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library (IEL) (UW System Shared)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Applied Sciences
EISBN 9781665466233
1665466235
EISSN 2471-8963
EndPage 6
ExternalDocumentID 9922726
Genre orig-research
GroupedDBID 6IE
6IF
6IH
6IK
6IL
6IN
AAJGR
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IPLJI
OCL
RIE
RIL
RNS
ID FETCH-LOGICAL-i203t-bf8fb83c5119954cd1f2c57b4c1b13ffedf150afe0c2d5d8797905e8fa5e7e4f3
IEDL.DBID RIE
ISICitedReferencesCount 1
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000886233300015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:27:07 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i203t-bf8fb83c5119954cd1f2c57b4c1b13ffedf150afe0c2d5d8797905e8fa5e7e4f3
PageCount 6
ParticipantIDs ieee_primary_9922726
PublicationCentury 2000
PublicationDate 2022-Sept.-11
PublicationDateYYYYMMDD 2022-09-11
PublicationDate_xml – month: 09
  year: 2022
  text: 2022-Sept.-11
  day: 11
PublicationDecade 2020
PublicationTitle European Workshop on Visual Information Processing
PublicationTitleAbbrev EUVIP
PublicationYear 2022
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0001611604
Score 2.2008965
Snippet Multimodal image coding often uses standard encoding algorithms, which do not exploit multimodality characteristics. This paper proposes a new cross-modality...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Computed tomography
Generative adversarial networks
Generative predictive coding
Image coding
Information processing
Learning based prediction
Lossless image coding
Multimodal image coding
Prediction algorithms
Versatile Video Coding
Video coding
Visualization
Title Lossless Coding of Multimodal Image Pairs Based on Image-To-Image Translation
URI https://ieeexplore.ieee.org/document/9922726
WOSCitedRecordID wos000886233300015&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
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pa8MgFJa27LBTt7Vjv_Gw42yjxhivKy0rdCWHtvRWEn1CoU1Gf-zvnyahZbDLbqI-BMX3qe_7ngi96lSw2HBKQHIgoRWCpFQByQKVWaApVyU3ZzGR02m8XKqkgd5OWhgAKMln0PPFMpZvCn30T2V9n0NVsqiJmlJGlVbr_J4SURoFYU3hooHqD-eLcSLcYF6Pwlivtv71jUqJIqP2_8a_Qt2zHA8nJ6C5Rg3Ib1C7Pj_ienfuO-hz4hBv41wXHhS-Jy4sLgW228KkGzzeOt-BEx_Awe8OvAwu8qqSzApStZbQVdHjumg-Gs4GH6T-LoGsWcAPJLOxzWKufWRQiVAbapkWMgs1zSi3Fox1p7_UQqCZESaWyifngtimAiSElt-iVl7kcIcwZak2xjIuqAlF5m7J7pakJA117CxSc486fnZWX1VGjFU9MQ9_Vz-iS78AnmVB6RNqHXZHeEYX-vuw3u9eymX8ARbInoU
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JawIxGA3WFtqTbbV0bw49NjrZnMm1RVE6yhxUvEkmCwg6U1z6-5vMDEqhl95CFhK-kLwk33tfAHhVkpNIU4xMSA1ilnMksTAoDURqDZZUFNycWRyOx9F8LpIaeDtoYYwxBfnMtH2y8OXrXO39U1nHx1ANSfcEnHLGSFCqtY4vKl2MuwGrSFw4EJ3edDZMuOvOK1IIaVftf32kUuBIv_G_EVyC1lGQB5MD1FyBmsmuQaM6QcJqfW6bYBQ7zFu5zQt-5L4mzC0sJLbrXMsVHK7d7gET78KB7w6-NMyzMhNNclSWFuBVEuRaYNrvTT4GqPowAS1JQHcotZFNI6q8b1BwpjS2RPEwZQqnmFprtHXnP2lNoIjmOgqFD89lIiu5CQ2z9AbUszwztwBiIpXWllCONeOpuye7e5IIMVORayH1HWh66yy-ypgYi8ow939nv4DzwWQUL-Lh-PMBXPjJ8JwLjB9BfbfZmydwpr53y-3muZjSH4VRocw
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%3Abook&rft.genre=proceeding&rft.title=European+Workshop+on+Visual+Information+Processing&rft.atitle=Lossless+Coding+of+Multimodal+Image+Pairs+Based+on+Image-To-Image+Translation&rft.au=Parracho%2C+Joao+O.&rft.au=Thomaz%2C+Lucas+A.&rft.au=Tavora%2C+Luis+M.+N.&rft.au=Assuncao%2C+Pedro+A.+A.&rft.date=2022-09-11&rft.pub=IEEE&rft.eissn=2471-8963&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FEUVIP53989.2022.9922726&rft.externalDocID=9922726