{"id":"https://openalex.org/W2018172162","doi":"https://doi.org/10.1109/tmm.2014.2368272","title":"A Novel No-Reference Video Quality Metric for Evaluating Temporal Jerkiness due to Frame Freezing","display_name":"A Novel No-Reference Video Quality Metric for Evaluating Temporal Jerkiness due to Frame Freezing","publication_year":2014,"publication_date":"2014-11-07","ids":{"openalex":"https://openalex.org/W2018172162","doi":"https://doi.org/10.1109/tmm.2014.2368272","mag":"2018172162"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2014.2368272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2014.2368272","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1411.1705","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuanyi Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yuanyi Xue","raw_affiliation_strings":["Department of Electrical and Computer Engineering, NYU Polytechnic School of Engineering, Brooklyn, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, NYU Polytechnic School of Engineering, Brooklyn, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Beril Erkin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beril Erkin","raw_affiliation_strings":["Department of Electrical and Computer Engineering, NYU Polytechnic School of Engineering, Brooklyn, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, NYU Polytechnic School of Engineering, Brooklyn, NY, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Yao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao Wang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, NYU Polytechnic School of Engineering, Brooklyn, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, NYU Polytechnic School of Engineering, Brooklyn, NY, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7175,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.92192234,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"17","issue":"1","first_page":"134","last_page":"139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9934999942779541,"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":0.9934999942779541,"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.00139999995008111,"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/frame","display_name":"Frame (networking)","score":0.7430999875068665},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7336000204086304},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5950000286102295},{"id":"https://openalex.org/keywords/video-quality","display_name":"Video quality","score":0.5236999988555908},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5110999941825867},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5059999823570251},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.503000020980835},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5023000240325928},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4821999967098236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7991999983787537},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.7430999875068665},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7336000204086304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6608999967575073},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5950000286102295},{"id":"https://openalex.org/C103910844","wikidata":"https://www.wikidata.org/wiki/Q2631256","display_name":"Video quality","level":3,"score":0.5236999988555908},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5059999823570251},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39730000495910645},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37229999899864197},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C204641915","wikidata":"https://www.wikidata.org/wiki/Q7315509","display_name":"Residual frame","level":4,"score":0.3100999891757965},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C65483669","wikidata":"https://www.wikidata.org/wiki/Q3536669","display_name":"Video processing","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2863999903202057},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.2581000030040741}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tmm.2014.2368272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2014.2368272","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1411.1705","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1411.1705","pdf_url":"https://arxiv.org/pdf/1411.1705","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1411.1705","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1411.1705","pdf_url":"https://arxiv.org/pdf/1411.1705","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1987259051","https://openalex.org/W2094097684","https://openalex.org/W2106517536","https://openalex.org/W2125654704","https://openalex.org/W2787894218","https://openalex.org/W6629477544"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"propose":[4],"a":[5,30,105,140,176],"novel":[6],"no-reference":[7],"(NR)":[8],"video":[9,117],"quality":[10,128],"metric":[11,28,167],"that":[12,37],"evaluates":[13],"the":[14,42,48,55,70,76,82,87,97,101,132,157,161],"impact":[15,43],"of":[16,44,57,78],"frame":[17,45,65,73],"freezing":[18,46],"due":[19],"to":[20,40,85],"either":[21],"packet":[22],"loss":[23],"or":[24],"late":[25],"arrival.":[26],"Our":[27,165],"uses":[29],"trained":[31],"neural":[32,83],"network":[33,84,98,124],"acting":[34],"on":[35,47],"features":[36,53,90],"are":[38],"chosen":[39],"capture":[41],"perceived":[49],"quality.":[50],"The":[51,119],"considered":[52],"include":[54],"number":[56],"freezes,":[58],"freeze":[59],"duration":[60],"statistics,":[61,64],"inter-freeze":[62],"distance":[63],"difference":[66],"before":[67],"and":[68,75,91,100,115,123,139,154,160,171],"after":[69],"freeze,":[71],"normal":[72],"difference,":[74],"ratio":[77],"them.":[79],"We":[80,95],"use":[81],"find":[86],"mapping":[88],"between":[89],"subjective":[92],"test":[93,137],"scores.":[94],"optimize":[96],"structure":[99,125],"feature":[102,121],"selection":[103],"through":[104],"cross-validation":[106],"procedure,":[107],"using":[108],"training":[109,133,158],"samples":[110],"extracted":[111],"from":[112],"both":[113,131],"VQEG":[114],"LIVE":[116],"databases.":[118],"resulting":[120],"set":[122,159],"yields":[126],"accurate":[127],"prediction":[129],"for":[130,156],"data":[134],"containing":[135],"54":[136],"videos":[138],"separate":[141],"testing":[142,162],"dataset":[143],"including":[144],"14":[145],"videos,":[146],"with":[147,178],"Pearson":[148],"correlation":[149],"coefficients":[150],"greater":[151],"than":[152],"0.9":[153],"0.8":[155],"set,":[163],"respectively.":[164],"proposed":[166],"has":[168],"low":[169],"complexity":[170],"could":[172],"be":[173],"utilized":[174],"in":[175],"system":[177],"real-time":[179],"processing":[180],"constraint.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2016-06-24T00:00:00"}