{"id":"https://openalex.org/W2887080587","doi":"https://doi.org/10.1109/tip.2018.2851670","title":"Deep Visual Discomfort Predictor for Stereoscopic 3D Images","display_name":"Deep Visual Discomfort Predictor for Stereoscopic 3D Images","publication_year":2018,"publication_date":"2018-06-29","ids":{"openalex":"https://openalex.org/W2887080587","doi":"https://doi.org/10.1109/tip.2018.2851670","mag":"2887080587","pmid":"https://pubmed.ncbi.nlm.nih.gov/29994709"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2018.2851670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2851670","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037805423","display_name":"Heeseok Oh","orcid":"https://orcid.org/0000-0002-0920-7281"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heeseok Oh","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Yonsei University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109466135","display_name":"Sewoong Ahn","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sewoong Ahn","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Yonsei University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100320181","display_name":"Sanghoon Lee","orcid":"https://orcid.org/0000-0001-9895-5347"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sanghoon Lee","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Yonsei University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-9895-5347","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075463806","display_name":"Alan C. Bovik","orcid":"https://orcid.org/0000-0001-6067-710X"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alan Conrad Bovik","raw_affiliation_strings":["Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.06,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.82638381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"27","issue":"11","first_page":"5420","last_page":"5432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11408","display_name":"Advanced Optical Imaging Technologies","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8123573064804077},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7952618598937988},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7878027558326721},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7523115873336792},{"id":"https://openalex.org/keywords/stereoscopy","display_name":"Stereoscopy","score":0.6185707449913025},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5265583992004395},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5145785212516785},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4970896542072296},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.45087265968322754},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4416269063949585},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4340932071208954},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4282287359237671},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41377022862434387},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3644371032714844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8123573064804077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7952618598937988},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7878027558326721},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7523115873336792},{"id":"https://openalex.org/C126057942","wikidata":"https://www.wikidata.org/wiki/Q35158","display_name":"Stereoscopy","level":2,"score":0.6185707449913025},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5265583992004395},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5145785212516785},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4970896542072296},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.45087265968322754},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4416269063949585},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4340932071208954},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4282287359237671},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41377022862434387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3644371032714844},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2018.2851670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2851670","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:29994709","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29994709","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6686027235","display_name":null,"funder_award_id":"2016R1A2B2014525","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W104184427","https://openalex.org/W1521705257","https://openalex.org/W1522301498","https://openalex.org/W1647993270","https://openalex.org/W1665214252","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1936373248","https://openalex.org/W1966124261","https://openalex.org/W1981076008","https://openalex.org/W1982471090","https://openalex.org/W1985162039","https://openalex.org/W1989215485","https://openalex.org/W1991012411","https://openalex.org/W1997247158","https://openalex.org/W1998684374","https://openalex.org/W1998761584","https://openalex.org/W2011711856","https://openalex.org/W2012757283","https://openalex.org/W2017598974","https://openalex.org/W2030294991","https://openalex.org/W2031223794","https://openalex.org/W2042959301","https://openalex.org/W2046083559","https://openalex.org/W2051596736","https://openalex.org/W2063855213","https://openalex.org/W2066065672","https://openalex.org/W2069784252","https://openalex.org/W2072441879","https://openalex.org/W2074883210","https://openalex.org/W2076063813","https://openalex.org/W2091260665","https://openalex.org/W2099308462","https://openalex.org/W2100879597","https://openalex.org/W2102166818","https://openalex.org/W2106276241","https://openalex.org/W2108598243","https://openalex.org/W2120838001","https://openalex.org/W2121513507","https://openalex.org/W2127133513","https://openalex.org/W2127418128","https://openalex.org/W2130427361","https://openalex.org/W2131524184","https://openalex.org/W2136339732","https://openalex.org/W2145725161","https://openalex.org/W2158659019","https://openalex.org/W2162692770","https://openalex.org/W2163605009","https://openalex.org/W2169037956","https://openalex.org/W2183871738","https://openalex.org/W2187089797","https://openalex.org/W2206823058","https://openalex.org/W2248599718","https://openalex.org/W2362296076","https://openalex.org/W2418033038","https://openalex.org/W2546302380","https://openalex.org/W2566149141","https://openalex.org/W2614183853","https://openalex.org/W2735451741","https://openalex.org/W2767836774","https://openalex.org/W2919115771","https://openalex.org/W2963048316","https://openalex.org/W2964121744","https://openalex.org/W3118608800","https://openalex.org/W6604254268","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6649878215","https://openalex.org/W6679667936","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W1536158975","https://openalex.org/W2393788985","https://openalex.org/W2157670837","https://openalex.org/W2029966401","https://openalex.org/W2374120702","https://openalex.org/W2288745733","https://openalex.org/W2631594184","https://openalex.org/W2072849536","https://openalex.org/W4295532600","https://openalex.org/W1983607852"],"abstract_inverted_index":{"Most":[0],"prior":[1],"approaches":[2],"to":[3,63,83,117,144,153,241],"the":[4,17,155,160,163,166,232],"problem":[5],"of":[6,19,27,31,70,78,92,158],"stereoscopic":[7],"3D":[8,192],"(S3D)":[9],"visual":[10,28,72,136,227],"discomfort":[11,137,193,228],"prediction":[12,138],"(VDP)":[13],"have":[14,42],"focused":[15],"on":[16,25,38,121,190,200,212,219],"extraction":[18],"perceptually":[20],"meaningful":[21],"handcrafted":[22],"features":[23,65,175,183],"based":[24,47],"models":[26],"perception":[29],"and":[30,102],"natural":[32],"depth":[33],"statistics.":[34],"Towards":[35],"advancing":[36],"performance":[37,238],"this":[39],"problem,":[40],"we":[41,87],"developed":[43],"a":[44,58,75,85,89,104,125,177],"deep":[45],"learning":[46,189],"VDP":[48,243],"model":[49,103,207,235],"named":[50],"Deep":[51],"Visual":[52],"Discomfort":[53],"Predictor":[54],"(DeepVDP).":[55],"DeepVDP":[56,234],"uses":[57],"convolutional":[59],"neural":[60],"network":[61],"(CNN)":[62],"learn":[64],"that":[66,129,208],"are":[67,151,171,184],"highly":[68],"predictive":[69],"experienced":[71],"discomfort.":[73],"Since":[74,112],"large":[76],"amount":[77],"reference":[79],"data":[80],"is":[81,114,130,142,216],"needed":[82],"train":[84],"CNN,":[86],"develop":[88],"systematic":[90],"way":[91],"dividing":[93],"S3D":[94,135,202,226],"image":[95],"into":[96,173],"local":[97,169],"regions":[98],"defined":[99],"as":[100,197,239],"patches,":[101],"patch-based":[105],"CNN":[106,206],"using":[107],"two":[108],"sequential":[109],"training":[110,159],"steps.":[111],"it":[113],"very":[115],"difficult":[116],"obtain":[118],"human":[119],"opinions":[120],"each":[122,145,201],"patch,":[123],"instead":[124],"proxy":[126,148,213],"ground-truth":[127,149,198],"label":[128],"generated":[131],"by":[132,231],"an":[133],"existing":[134],"algorithm":[139],"called":[140],"3D-VDP":[141],"assigned":[143],"patch.":[146],"These":[147],"labels":[150,199,215],"used":[152],"conduct":[154],"first":[156],"stage":[157],"CNN.":[161],"In":[162],"second":[164],"stage,":[165],"automatically":[167],"learned":[168,182],"abstractions":[170],"aggregated":[172],"global":[174,221,225],"via":[176,187],"feature":[178],"aggregation":[179],"layer.":[180],"The":[181,204,224],"iteratively":[185],"updated":[186],"supervised":[188],"subjective":[191,222],"scores,":[194],"which":[195],"serve":[196],"image.":[203],"patchbased":[205],"has":[209],"been":[210],"pretrained":[211],"groundtruth":[214],"subsequently":[217],"retrained":[218],"true":[220],"scores.":[223],"scores":[229],"predicted":[230],"trained":[233],"achieve":[236],"state-of-the-art":[237],"compared":[240],"previous":[242],"algorithms.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}