{"id":"https://openalex.org/W2720011622","doi":"https://doi.org/10.2352/issn.2470-1173.2017.13.ipas-202","title":"Full-reference metrics multidistortional analysis","display_name":"Full-reference metrics multidistortional analysis","publication_year":2017,"publication_date":"2017-01-29","ids":{"openalex":"https://openalex.org/W2720011622","doi":"https://doi.org/10.2352/issn.2470-1173.2017.13.ipas-202","mag":"2720011622"},"language":"en","primary_location":{"id":"doi:10.2352/issn.2470-1173.2017.13.ipas-202","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2017.13.ipas-202","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-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/A5028393131","display_name":"\u041e\u043b\u0435\u0433 \u0404\u0440\u0435\u043c\u0435\u0454\u0432","orcid":"https://orcid.org/0000-0001-7865-0570"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oleg Ieremeiev","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011325512","display_name":"Vladimir Lukin","orcid":"https://orcid.org/0000-0002-1443-9685"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vladimir Lukin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088092183","display_name":"Nikolay Ponomarenko","orcid":"https://orcid.org/0000-0001-9611-7542"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nikolay Ponomarenko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090213299","display_name":"Karen Egiazarian","orcid":"https://orcid.org/0000-0002-8135-1085"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karen Egiazarian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1804,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55265274,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"29","issue":"13","first_page":"27","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.8080999851226807,"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":0.8080999851226807,"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/T14512","display_name":"Technology and Human Factors in Education and Health","score":0.022099999710917473,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.013899999670684338,"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/metric","display_name":"Metric (unit)","score":0.8566014766693115},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6817963123321533},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6788539886474609},{"id":"https://openalex.org/keywords/spearmans-rank-correlation-coefficient","display_name":"Spearman's rank correlation coefficient","score":0.6074426174163818},{"id":"https://openalex.org/keywords/rank-correlation","display_name":"Rank correlation","score":0.5997568964958191},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.536586344242096},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4724369943141937},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47012007236480713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4227049946784973},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3901601731777191},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32952845096588135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24203115701675415},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09539273381233215},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.06692659854888916},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.06692308187484741}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.8566014766693115},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6817963123321533},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6788539886474609},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.6074426174163818},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.5997568964958191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.536586344242096},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4724369943141937},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47012007236480713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4227049946784973},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3901601731777191},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32952845096588135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24203115701675415},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09539273381233215},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.06692659854888916},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.06692308187484741},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/issn.2470-1173.2017.13.ipas-202","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2017.13.ipas-202","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2096962026","https://openalex.org/W2562018983","https://openalex.org/W3085398489","https://openalex.org/W4232475722","https://openalex.org/W4361792233","https://openalex.org/W2949627706","https://openalex.org/W3097028157","https://openalex.org/W2906869600","https://openalex.org/W2463069270","https://openalex.org/W1996881917"],"abstract_inverted_index":{"This":[0,45],"paper":[1],"is":[2,21,104,115],"devoted":[3],"to":[4,47,55,92],"analysis":[5],"and":[6,37],"further":[7,109],"improvement":[8,110],"of":[9,12,18,32,41,51,96,111],"full-reference":[10],"metrics":[11,75,98],"image":[13],"visual":[14,58],"quality.":[15],"The":[16],"effectiveness":[17],"a":[19,52,56],"metric":[20,43,54,114],"characterized":[22],"by":[23],"the":[24,29,38,49,64,72,94,97,112],"rank":[25],"correlation":[26,70],"factors":[27],"between":[28],"obtained":[30,62],"array":[31,40],"mean":[33],"opinion":[34],"scores":[35],"(MOS)":[36],"corresponding":[39],"given":[42],"values.":[44],"allows":[46],"determine":[48],"correspondence":[50],"considered":[53],"human":[57],"system":[59],"(HVS).":[60],"Results":[61],"on":[63],"database":[65],"TID2013":[66],"show":[67],"that":[68,90],"Spearman":[69],"for":[71,108],"best":[73],"existing":[74],"(PSNRHMA,":[76],"FSIM,":[77],"SFF,":[78],"etc.)":[79],"does":[80],"not":[81],"exceed":[82],"0.85.":[83],"In":[84],"this":[85],"paper,":[86],"extended":[87],"verification":[88],"tools":[89],"allow":[91],"detect":[93],"shortcomings":[95],"taking":[99],"into":[100],"account":[101],"combined":[102],"distortions":[103],"proposed.":[105],"An":[106],"example":[107],"PSNRHMA":[113],"presented.":[116]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
