{"id":"https://openalex.org/W4391306357","doi":"https://doi.org/10.1109/vcip59821.2023.10402715","title":"Binocular Rivalry and Fusion Mechanisms Based No-Reference Stereoscopic Image Quality Assessment Considering Feedback Guidance","display_name":"Binocular Rivalry and Fusion Mechanisms Based No-Reference Stereoscopic Image Quality Assessment Considering Feedback Guidance","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4391306357","doi":"https://doi.org/10.1109/vcip59821.2023.10402715"},"language":"en","primary_location":{"id":"doi:10.1109/vcip59821.2023.10402715","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vcip59821.2023.10402715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5113168092","display_name":"Haoxiang Chang","orcid":"https://orcid.org/0009-0002-7979-0801"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoxiang Chang","raw_affiliation_strings":["Tianjin University,Tianjin,China","Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036035213","display_name":"Sumei Li","orcid":"https://orcid.org/0000-0002-4793-3161"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sumei Li","raw_affiliation_strings":["Tianjin University,Tianjin,China","Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028982262","display_name":"Dongyue He","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyue He","raw_affiliation_strings":["Tianjin University,Tianjin,China","Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100374071","display_name":"Chen Zhang","orcid":"https://orcid.org/0000-0001-8556-0186"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Zhang","raw_affiliation_strings":["Tianjin University,Tianjin,China","Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5113168092"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28430057,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9771999716758728,"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"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9771999716758728,"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"}},{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.949999988079071,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/binocular-rivalry","display_name":"Binocular rivalry","score":0.9015295505523682},{"id":"https://openalex.org/keywords/stereoscopy","display_name":"Stereoscopy","score":0.7509772777557373},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7394319772720337},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6131654977798462},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.594699501991272},{"id":"https://openalex.org/keywords/rivalry","display_name":"Rivalry","score":0.5888738632202148},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.5747511982917786},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5598235726356506},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.48114216327667236},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4806862771511078},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4012448489665985},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.1590290665626526},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.12551096081733704},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11199420690536499},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.05995580554008484}],"concepts":[{"id":"https://openalex.org/C198313034","wikidata":"https://www.wikidata.org/wiki/Q864009","display_name":"Binocular rivalry","level":4,"score":0.9015295505523682},{"id":"https://openalex.org/C126057942","wikidata":"https://www.wikidata.org/wiki/Q35158","display_name":"Stereoscopy","level":2,"score":0.7509772777557373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394319772720337},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6131654977798462},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.594699501991272},{"id":"https://openalex.org/C2779602485","wikidata":"https://www.wikidata.org/wiki/Q2901966","display_name":"Rivalry","level":2,"score":0.5888738632202148},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.5747511982917786},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5598235726356506},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.48114216327667236},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4806862771511078},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4012448489665985},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.1590290665626526},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.12551096081733704},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11199420690536499},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.05995580554008484},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip59821.2023.10402715","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vcip59821.2023.10402715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.5099999904632568,"display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320330944","display_name":"Nature","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1857008024","https://openalex.org/W1981076008","https://openalex.org/W2018423774","https://openalex.org/W2019377328","https://openalex.org/W2067554686","https://openalex.org/W2073812468","https://openalex.org/W2099308462","https://openalex.org/W2476548250","https://openalex.org/W2810874584","https://openalex.org/W2898570661","https://openalex.org/W2904946794","https://openalex.org/W2905908087","https://openalex.org/W2920014186","https://openalex.org/W2964701660","https://openalex.org/W3000373634","https://openalex.org/W3002782581","https://openalex.org/W3025644747","https://openalex.org/W3093779980","https://openalex.org/W3160594468","https://openalex.org/W3187196322","https://openalex.org/W3213678819","https://openalex.org/W4205178014","https://openalex.org/W4205990857","https://openalex.org/W4211057129","https://openalex.org/W4220758784","https://openalex.org/W4223489487","https://openalex.org/W4226236776","https://openalex.org/W4297775537","https://openalex.org/W6636142342","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W1975528811","https://openalex.org/W2483752837","https://openalex.org/W4211057129","https://openalex.org/W2109121715","https://openalex.org/W2121070606","https://openalex.org/W1556105300","https://openalex.org/W2005630652","https://openalex.org/W2057359658","https://openalex.org/W2033007170","https://openalex.org/W4385893425"],"abstract_inverted_index":{"With":[0],"the":[1,43,80,87,103,126,138],"growing":[2],"popularity":[3],"of":[4,42,55,67,83,111,128,140],"3D":[5],"content":[6],"and":[7,39,74,96,115],"virtual":[8],"reality":[9],"applications,":[10],"effective":[11],"no-reference":[12],"stereoscopic":[13],"image":[14],"quality":[15],"assessment":[16],"(NR-SIQA)":[17],"methods":[18],"have":[19],"become":[20],"increasingly":[21],"important.":[22],"In":[23,48],"this":[24],"paper,":[25],"we":[26,58,117],"propose":[27,59],"a":[28,52],"convolutional":[29],"neural":[30],"network":[31],"(CNN)":[32],"SIQA":[33],"model":[34],"based":[35,101],"on":[36,102],"binocular":[37,56,75,84],"rivalry":[38,85],"fusion":[40],"mechanisms":[41],"human":[44],"visual":[45],"system":[46],"(HVS).":[47],"order":[49],"to":[50,124,131],"get":[51],"better":[53],"representation":[54],"information,":[57],"Two-Stage":[60],"Enhanced":[61],"Fusion":[62,92],"Module":[63,93],"(TSEFM)":[64],"that":[65,108],"consists":[66],"two":[68],"stages":[69],"for":[70],"monocular":[71],"features":[72,76,130],"enhancement":[73],"fusion,":[77],"respectively.":[78],"Given":[79],"dynamical":[81],"characteristics":[82],"phenomenon,":[86],"proposed":[88],"Content-Aware":[89],"Binocular":[90],"Rivalry":[91],"(CABRFM)":[94],"dynamically":[95],"adaptively":[97],"adjusts":[98],"its":[99,147],"output":[100],"input":[104],"content.":[105],"Additionally,":[106],"considering":[107],"feedback":[109,119],"mechanism":[110],"HVS":[112],"is":[113],"indispensable":[114],"significant,":[116],"introduce":[118],"connections":[120],"during":[121],"feature":[122],"aggregation":[123],"realize":[125],"guidance":[127],"high-level":[129],"low-level":[132],"features.":[133],"Extensive":[134],"experimental":[135],"results":[136],"demonstrate":[137],"superiority":[139],"our":[141],"method":[142],"over":[143],"state-of-the-art":[144],"metrics,":[145],"showcasing":[146],"excellent":[148],"performance.":[149]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
