{"id":"https://openalex.org/W2164287382","doi":"https://doi.org/10.1109/tip.2012.2236343","title":"Image Quality Assessment Using Multi-Method Fusion","display_name":"Image Quality Assessment Using Multi-Method Fusion","publication_year":2012,"publication_date":"2012-12-24","ids":{"openalex":"https://openalex.org/W2164287382","doi":"https://doi.org/10.1109/tip.2012.2236343","mag":"2164287382","pmid":"https://pubmed.ncbi.nlm.nih.gov/23288335"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2012.2236343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2012.2236343","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/A5023526360","display_name":"Tsung-Jung Liu","orcid":"https://orcid.org/0000-0003-4296-0942"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tsung-Jung Liu","raw_affiliation_strings":["Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA. liut@usc.edu;cckuo@sipi.usc.edu","Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA. liut@usc.edu;cckuo@sipi.usc.edu","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403129","display_name":"Weisi Lin","orcid":"https://orcid.org/0000-0001-9866-1947"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Weisi Lin","raw_affiliation_strings":["School of Computer Engineering, Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001082656","display_name":"C.\u2010C. Jay Kuo","orcid":"https://orcid.org/0000-0001-9474-5035"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"C.-C. Jay Kuo","raw_affiliation_strings":["Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":12.2563,"has_fulltext":false,"cited_by_count":196,"citation_normalized_percentile":{"value":0.99033993,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"22","issue":"5","first_page":"1793","last_page":"1807"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9994999766349792,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9959999918937683,"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/support-vector-machine","display_name":"Support vector machine","score":0.6843165159225464},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6549854278564453},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6113078594207764},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6098473072052002},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5381404161453247},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5120754837989807},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5071516036987305},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4940202832221985},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4859072268009186},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.47900694608688354},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4450230896472931},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.43495485186576843},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4340868890285492},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41512203216552734},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32150575518608093},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2187994122505188},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13332900404930115}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6843165159225464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6549854278564453},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6113078594207764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6098473072052002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5381404161453247},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5120754837989807},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5071516036987305},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4940202832221985},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4859072268009186},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.47900694608688354},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4450230896472931},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.43495485186576843},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4340868890285492},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41512203216552734},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32150575518608093},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2187994122505188},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13332900404930115},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2012.2236343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2012.2236343","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:23288335","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/23288335","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1581676061","https://openalex.org/W1840338487","https://openalex.org/W1964357740","https://openalex.org/W1973207880","https://openalex.org/W2009272644","https://openalex.org/W2025769979","https://openalex.org/W2039562180","https://openalex.org/W2046119925","https://openalex.org/W2103116581","https://openalex.org/W2107476778","https://openalex.org/W2114582993","https://openalex.org/W2125312659","https://openalex.org/W2129972025","https://openalex.org/W2131483079","https://openalex.org/W2133665775","https://openalex.org/W2141983208","https://openalex.org/W2142884912","https://openalex.org/W2144468361","https://openalex.org/W2153635508","https://openalex.org/W2157316755","https://openalex.org/W2159269332","https://openalex.org/W2159311532","https://openalex.org/W2160600136","https://openalex.org/W2163370434","https://openalex.org/W2171349048","https://openalex.org/W2270330859","https://openalex.org/W2291445757","https://openalex.org/W2729302458","https://openalex.org/W4205687621","https://openalex.org/W4212863985","https://openalex.org/W4237171445","https://openalex.org/W6679752383","https://openalex.org/W6740330286"],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2017776670","https://openalex.org/W2952760143","https://openalex.org/W2347897961","https://openalex.org/W2979236518","https://openalex.org/W2358318464","https://openalex.org/W2340870721","https://openalex.org/W2040975418"],"abstract_inverted_index":{"A":[0],"new":[1,49],"methodology":[2],"for":[3],"objective":[4],"image":[5],"quality":[6,153],"assessment":[7,154],"(IQA)":[8],"with":[9,64],"multi-method":[10],"fusion":[11,160],"(MMF)":[12],"is":[13,20,27,52,101,110,117,146,170],"presented":[14],"in":[15,37,108,158,188],"this":[16],"paper.":[17],"The":[18,48,144,162],"research":[19],"motivated":[21],"by":[22,68,119,181],"the":[23,34,56,76,91,113,127,140,159],"observation":[24],"that":[25,31],"there":[26],"no":[28],"single":[29],"method":[30,142,165],"can":[32],"give":[33],"best":[35],"performance":[36],"all":[38],"situations.":[39],"To":[40,124],"achieve":[41],"MMF,":[42,130],"we":[43,80,131],"adopt":[44],"a":[45,69,120,136,174,182],"regression":[46,77,96,169],"approach.":[47,123],"MMF":[50,164],"score":[51],"set":[53],"to":[54,74,86,111,134,172],"be":[55],"nonlinear":[57],"combination":[58],"of":[59,129,177],"scores":[60],"from":[61,139],"multiple":[62],"methods":[63,155,180],"suitable":[65],"weights":[66],"obtained":[67],"training":[70],"process.":[71,161],"In":[72],"order":[73],"improve":[75],"results":[78],"further,":[79],"divide":[81],"distorted":[82],"images":[83],"into":[84],"three":[85,152],"five":[87],"groups":[88],"based":[89],"on":[90],"distortion":[92],"types":[93],"and":[94],"perform":[95,132],"within":[97],"each":[98],"group,":[99],"which":[100,116],"called":[102],"\"context-dependent":[103],"MMF\"":[104],"(CD-MMF).":[105],"One":[106],"task":[107],"CD-MMF":[109],"determine":[112],"context":[114],"automatically,":[115],"achieved":[118],"machine":[121],"learning":[122],"further":[125],"reduce":[126],"complexity":[128],"algorithms":[133],"select":[135],"small":[137],"subset":[138],"candidate":[141],"set.":[143],"result":[145],"very":[147],"good":[148],"even":[149],"if":[150],"only":[151],"are":[156],"included":[157],"proposed":[163],"using":[166],"support":[167],"vector":[168],"shown":[171],"outperform":[173],"large":[175],"number":[176],"existing":[178],"IQA":[179],"significant":[183],"margin":[184],"when":[185],"being":[186],"tested":[187],"six":[189],"representative":[190],"databases.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":23},{"year":2019,"cited_by_count":25},{"year":2018,"cited_by_count":14},{"year":2017,"cited_by_count":18},{"year":2016,"cited_by_count":15},{"year":2015,"cited_by_count":21},{"year":2014,"cited_by_count":18},{"year":2013,"cited_by_count":6}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
