{"id":"https://openalex.org/W3202581966","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534406","title":"No-reference Image Quality Assessment Based on Multi-scale Convolutional Neural Network Assisted with Visual Saliency","display_name":"No-reference Image Quality Assessment Based on Multi-scale Convolutional Neural Network Assisted with Visual Saliency","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3202581966","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534406","mag":"3202581966"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5040335415","display_name":"Huajie Wang","orcid":"https://orcid.org/0000-0002-6173-672X"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huajie Wang","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090793360","display_name":"Mei Lil","orcid":null},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mei Lil","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100755955","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0003-3068-1583"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["NPPA Key Laboratory of Publishing Integration Development, ECNUP, Shanghai, China","School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"NPPA Key Laboratory of Publishing Integration Development, ECNUP, Shanghai, China","institution_ids":[]},{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5040335415"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49488562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9998999834060669,"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":0.9998999834060669,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9952999949455261,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9947999715805054,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.848506510257721},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7889159321784973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7694979906082153},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6050816178321838},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.6041653752326965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5717371702194214},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5432530641555786},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.523000180721283},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5123642683029175},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.500652551651001},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4999654293060303},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4856933057308197},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44242382049560547}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.848506510257721},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7889159321784973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7694979906082153},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6050816178321838},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.6041653752326965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5717371702194214},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5432530641555786},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.523000180721283},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5123642683029175},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.500652551651001},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4999654293060303},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4856933057308197},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44242382049560547},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W874645128","https://openalex.org/W1665214252","https://openalex.org/W1904365287","https://openalex.org/W1982471090","https://openalex.org/W2046119925","https://openalex.org/W2051596736","https://openalex.org/W2061513831","https://openalex.org/W2063360098","https://openalex.org/W2071346414","https://openalex.org/W2073623229","https://openalex.org/W2086708275","https://openalex.org/W2111425436","https://openalex.org/W2115368080","https://openalex.org/W2124562516","https://openalex.org/W2127230474","https://openalex.org/W2133665775","https://openalex.org/W2138088051","https://openalex.org/W2141983208","https://openalex.org/W2161907179","https://openalex.org/W2162220380","https://openalex.org/W2162692770","https://openalex.org/W2163370434","https://openalex.org/W2171220523","https://openalex.org/W2294857031","https://openalex.org/W2324971623","https://openalex.org/W2509123681","https://openalex.org/W2518488994","https://openalex.org/W2556068545","https://openalex.org/W2566149141","https://openalex.org/W2568628164","https://openalex.org/W2572961056","https://openalex.org/W2616017856","https://openalex.org/W2749468216","https://openalex.org/W2761499647","https://openalex.org/W2792845633","https://openalex.org/W2793788258","https://openalex.org/W2807793257","https://openalex.org/W2889592295","https://openalex.org/W2902011469","https://openalex.org/W2905544033","https://openalex.org/W2963420686","https://openalex.org/W4239147634","https://openalex.org/W4247049427","https://openalex.org/W6637242042","https://openalex.org/W6663470086","https://openalex.org/W6679022428","https://openalex.org/W6701249984","https://openalex.org/W6743731764","https://openalex.org/W6757118075"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W4243305035","https://openalex.org/W1537496349","https://openalex.org/W2379407973","https://openalex.org/W2005223122"],"abstract_inverted_index":{"Nowadays,":[0],"methods":[1,31,39,75,119,230],"based":[2],"on":[3],"deep":[4],"learning":[5],"have":[6,17,33],"achieved":[7],"state-of-the-art":[8,232],"performance":[9],"in":[10,70,102,133,161],"image":[11,43,45,78,83,89,137,144,196,202],"quality":[12,93],"assessment":[13],"(IQA).":[14],"Many":[15],"efforts":[16],"been":[18],"made":[19],"to":[20,111,186,190,194,205,210,225],"design":[21,120],"convolutional":[22],"neural":[23,121],"network":[24,122],"(CNN)":[25],"for":[26,51],"IQA.":[27],"However,":[28],"current":[29],"CNN-based":[30,38,74,118,151],"mainly":[32],"the":[34,42,48,81,88,91,96,100,103,106,115,125,131,140,201,206,211,218],"following":[35],"shortcomings.":[36],"(1)":[37],"usually":[40],"segment":[41],"into":[44,84,169],"patches":[46,79,90,203],"of":[47,65,80,105,114,143,220],"same":[49,92,126],"size":[50],"data":[52],"enhancement,":[53],"which":[54,157],"cannot":[55],"retain":[56],"both":[57],"local":[58],"spatial":[59,63],"information":[60,64,142],"and":[61,86,139,180,231,239],"global":[62],"images":[66],"with":[67,124,154],"large":[68],"differences":[69],"resolution.":[71],"(2)":[72],"Most":[73],"input":[76,204],"all":[77],"entire":[82,97],"CNN":[85,172,189,208],"assign":[87,191],"score":[94],"as":[95],"image,":[98],"ignoring":[99],"difference":[101,132],"attention":[104],"human":[107],"visual":[108,155,212],"system":[109],"(HVS)":[110],"different":[112,136],"regions":[113],"image.":[116],"(3)":[117],"structures":[123],"channel":[127],"weight":[128,193],"without":[129],"considering":[130],"correlation":[134],"between":[135],"channels":[138],"key":[141],"patches.":[145],"Thus,":[146],"we":[147,158,199],"propose":[148],"a":[149,170,174],"multi-scale":[150,171,207],"model":[152],"assisted":[153],"saliency,":[156],"call":[159],"MS-SECNN":[160],"this":[162],"paper.":[163],"We":[164],"fuse":[165],"two":[166,240],"single-scale":[167,188],"CNNs":[168],"through":[173],"fully":[175],"connected":[176],"layer.":[177],"The":[178],"Squeeze":[179],"Excitation":[181],"(SE)":[182],"module":[183],"is":[184],"embedded":[185],"each":[187,195],"corresponding":[192],"channel.":[197],"Moreover,":[198],"select":[200],"according":[209],"saliency":[213],"map.":[214],"Experiments":[215],"results":[216],"validate":[217],"effectiveness":[219],"our":[221],"proposed":[222],"method":[223],"compared":[224],"typical":[226],"full-reference":[227],"(FR)":[228],"IQA":[229,235],"no-reference":[233],"(NR)":[234],"methods.":[236],"Our":[237],"code":[238],"self-built":[241],"datasets":[242],"are":[243],"publicly":[244],"available":[245],"<sup":[246],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[247],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[248]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}