LEADER 02535nam  2200349   450 
001 9910595456303321
005 20230831004721.0
035   $a(CKB)5690000000037332
035   $a(NjHacI)995690000000037332
035   $a(EXLCZ)995690000000037332
100   $a20230831d2022      uy             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aProceedings of the 2nd Workshop on Quality of Experience in Visual Multimedia Applications /$fJing Li
210  1$aNew York, NY, United States :$cAssociation for Computing Machinery,$d2022.
215   $a1 online resource (75 pages)
225 0 $aACM Conferences
311   $a1-4503-9499-X 
330   $aIt is our great pleasure to welcome you to ACM Multimedia 2022 Workshop on Quality of Experience in Visual Multimedia Applications - QoEVMA22. This year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports on leading edge issues of Quality of Experience (QoE) on any visual multimedia, including QoE assessment on VoD for movies, dramas, variety shows, User Generated Contents(UGC) on social networks, live streaming videos for gaming/shopping/social, etc; QoE assessment for different video formats in multimedia services, including 2D, stereoscopic 3D, High Dynamic Range (HDR), Augmented Reality (AR), Virtual Reality (VR), 360, Free-Viewpoint Video(FVV), etc, and Key performance indicators (KPI) analysis for QoE.  The mission of the workshop is to share novel idea or solution that fulfill the users' new requirements on quality of experience on advanced multimedia technology, and identify new directions for future research and development. The call for papers attracted submissions from China, Norway, France, Poland, India, Brazil, Morocco, and the United States. This year, we have received in total 14 submissions, and 8 papers were accepted.  We also encourage attendees to attend the invited keynote, which is given by prof. Mylene Farias from University of Brasilia, with the title of Estimating the Quality of Experience of Immersive Contents.
606   $aMachine learning$vCongresses
615  0$aMachine learning
676   $a006.31
700   $aLi$b Jing$0651690
801  0$bNjHacI
801  1$bNjHacl
906   $aBOOK
912   $a9910595456303321
996   $aProceedings of the 2nd Workshop on Quality of Experience in Visual Multimedia Applications$93530761
997   $aUNINA