{"id":"https://openalex.org/W4386597206","doi":"https://doi.org/10.1109/icip49359.2023.10221996","title":"Prnet: A Progressive Regression Network for No-Reference User-Generated-Content (UGC) Video Quality Assessment","display_name":"Prnet: A Progressive Regression Network for No-Reference User-Generated-Content (UGC) Video Quality Assessment","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386597206","doi":"https://doi.org/10.1109/icip49359.2023.10221996"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10221996","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10221996","pdf_url":"https://ieeexplore.ieee.org/ielx7/10221937/10221892/10221996.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/10221937/10221892/10221996.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100397733","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0003-1048-7994"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["NetEase"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NetEase","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100735306","display_name":"Bo Jiang","orcid":"https://orcid.org/0009-0000-7143-3733"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Jiang","raw_affiliation_strings":["Zhejiang Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Lab","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018763926","display_name":"Kailin Wu","orcid":"https://orcid.org/0009-0006-4122-5718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kailin Wu","raw_affiliation_strings":["ByteDance"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ByteDance","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12580841,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"640","last_page":"644"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"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/T11165","display_name":"Image and Video Quality Assessment","score":1.0,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9909999966621399,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9901999831199646,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8096237182617188},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.6366727948188782},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.61018967628479},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5652003288269043},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5116620063781738},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.4941442310810089},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4647921919822693},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44788050651550293},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4433586895465851},{"id":"https://openalex.org/keywords/video-quality","display_name":"Video quality","score":0.4405350685119629},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43763384222984314},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43706804513931274},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.423380970954895},{"id":"https://openalex.org/keywords/subjective-video-quality","display_name":"Subjective video quality","score":0.4204736351966858},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41156232357025146},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3739240765571594},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33007311820983887},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.18104833364486694},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17182090878486633},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13046932220458984},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0836590826511383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8096237182617188},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.6366727948188782},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.61018967628479},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5652003288269043},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5116620063781738},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4941442310810089},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4647921919822693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44788050651550293},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4433586895465851},{"id":"https://openalex.org/C103910844","wikidata":"https://www.wikidata.org/wiki/Q2631256","display_name":"Video quality","level":3,"score":0.4405350685119629},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43763384222984314},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43706804513931274},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.423380970954895},{"id":"https://openalex.org/C114227958","wikidata":"https://www.wikidata.org/wiki/Q7631422","display_name":"Subjective video quality","level":4,"score":0.4204736351966858},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41156232357025146},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3739240765571594},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33007311820983887},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.18104833364486694},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17182090878486633},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13046932220458984},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0836590826511383},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10221996","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10221996","pdf_url":"https://ieeexplore.ieee.org/ielx7/10221937/10221892/10221996.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/icip49359.2023.10221996","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10221996","pdf_url":"https://ieeexplore.ieee.org/ielx7/10221937/10221892/10221996.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386597206.pdf","grobid_xml":"https://content.openalex.org/works/W4386597206.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1982471090","https://openalex.org/W2117535912","https://openalex.org/W2129644086","https://openalex.org/W2147268297","https://openalex.org/W2169037956","https://openalex.org/W2194363988","https://openalex.org/W2194775991","https://openalex.org/W2475334473","https://openalex.org/W2611434713","https://openalex.org/W2791258091","https://openalex.org/W2939995367","https://openalex.org/W2950154603","https://openalex.org/W2963918210","https://openalex.org/W2965644659","https://openalex.org/W2970478035","https://openalex.org/W3030701471","https://openalex.org/W3090101394","https://openalex.org/W3090952760","https://openalex.org/W3099047215","https://openalex.org/W3103745061","https://openalex.org/W3163556915","https://openalex.org/W3174722860"],"related_works":["https://openalex.org/W2090097149","https://openalex.org/W4388138958","https://openalex.org/W1996919562","https://openalex.org/W2046455617","https://openalex.org/W4390244677","https://openalex.org/W2809031010","https://openalex.org/W2061696964","https://openalex.org/W2546999347","https://openalex.org/W3194854785","https://openalex.org/W3014439586"],"abstract_inverted_index":{"Objectively":[0],"perceptual":[1],"quality":[2,31],"assessment":[3,32],"of":[4,17,40],"UGC":[5],"videos":[6,54],"is":[7,27,73,114,153],"a":[8,99,111,146],"challenge":[9],"due":[10],"to":[11,138,175],"many":[12],"reasons.":[13],"First,":[14],"the":[15,24,28,42,53,108,118,122,140,143],"sources":[16],"UGC-videos":[18],"are":[19,44,55],"not":[20,45,56,59,88],"available,":[21],"which":[22,116,155],"makes":[23],"appropriate":[25],"technique":[26],"no-reference":[29],"video":[30],"(NR-VQA).":[33],"Second,":[34],"subjective":[35,129],"mean":[36],"option":[37],"scores":[38,132],"(MOS)":[39],"all":[41,90],"UGC-datasets":[43],"uniformly":[46],"distributed.":[47],"Third,":[48],"authentic":[49],"degradations":[50],"occurred":[51],"in":[52,104,192],"unique,":[57],"therefore,":[58],"predicable.":[60],"For":[61,107,142],"example,":[62],"an":[63],"over-":[64],"or":[65,83],"underexposure":[66],"video,":[67],"brightness":[68],"and":[69,148,166,180],"contrast":[70],"static":[71],"information":[72],"critical":[74],"for":[75],"evaluation.":[76],"Only":[77],"employing":[78],"verified":[79],"priori":[80,178],"statistic":[81],"knowledge":[82,86,179],"generalized":[84],"learning":[85,182],"may":[87],"cover":[89],"possible":[91],"distortions.":[92],"To":[93],"solve":[94],"these":[95],"problems,":[96],"we":[97],"introduce":[98],"framework\u2014Progressive":[100],"Regression":[101],"Network":[102],"(PRNet)":[103],"this":[105],"paper.":[106],"MOS":[109],"problem,":[110],"PR":[112],"module":[113,152],"proposed,":[115],"utilizes":[117],"coarse-to-fine":[119],"strategy":[120],"during":[121],"training":[123],"process.":[124],"It":[125],"can":[126],"turn":[127],"sparse":[128],"human":[130],"rating":[131],"into":[133],"integers":[134],"with":[135],"denser":[136],"samples":[137],"address":[139],"problem.":[141],"unpredictable":[144],"distortions,":[145],"wide":[147],"deep":[149,172],"feature":[150],"extraction":[151],"developed,":[154],"employs":[156],"both":[157],"low-level":[158],"features":[159,169],"generated":[160],"from":[161],"natural":[162],"scene":[163],"statistics":[164],"(NSS)":[165],"high-level":[167],"semantic":[168],"extracted":[170],"by":[171],"neural":[173],"networks,":[174],"fuse":[176],"memorizing":[177],"generalizing":[181],"features.":[183],"Experiments":[184],"demonstrate":[185],"that":[186],"our":[187],"PRNet":[188],"achieves":[189],"top":[190],"performance":[191],"three":[193],"main":[194],"datasets.":[195]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}