{"id":"https://openalex.org/W4404056544","doi":"https://doi.org/10.1109/tcsi.2024.3486243","title":"FastGW: A Machine Learning-Based Early Skip for the AV1 Global Warped Motion Compensation","display_name":"FastGW: A Machine Learning-Based Early Skip for the AV1 Global Warped Motion Compensation","publication_year":2024,"publication_date":"2024-11-05","ids":{"openalex":"https://openalex.org/W4404056544","doi":"https://doi.org/10.1109/tcsi.2024.3486243"},"language":"en","primary_location":{"id":"doi:10.1109/tcsi.2024.3486243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2024.3486243","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"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 Circuits and Systems I: Regular Papers","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/A5084737604","display_name":"William Kolodziejski","orcid":"https://orcid.org/0000-0002-8769-6930"},"institutions":[{"id":"https://openalex.org/I169248161","display_name":"Universidade Federal de Pelotas","ror":"https://ror.org/05msy9z54","country_code":"BR","type":"education","lineage":["https://openalex.org/I169248161"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"William Kolodziejski","raw_affiliation_strings":["Centro de Desenvolvimento Tecnol&#x00F3;gico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-8769-6930","affiliations":[{"raw_affiliation_string":"Centro de Desenvolvimento Tecnol&#x00F3;gico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Brazil","institution_ids":["https://openalex.org/I169248161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025772303","display_name":"Robson Domanski","orcid":"https://orcid.org/0000-0002-6339-0603"},"institutions":[{"id":"https://openalex.org/I169248161","display_name":"Universidade Federal de Pelotas","ror":"https://ror.org/05msy9z54","country_code":"BR","type":"education","lineage":["https://openalex.org/I169248161"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Robson Domanski","raw_affiliation_strings":["Centro de Desenvolvimento Tecnol&#x00F3;gico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centro de Desenvolvimento Tecnol&#x00F3;gico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Brazil","institution_ids":["https://openalex.org/I169248161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070735330","display_name":"Luciano Agostini","orcid":"https://orcid.org/0000-0002-3421-5830"},"institutions":[{"id":"https://openalex.org/I169248161","display_name":"Universidade Federal de Pelotas","ror":"https://ror.org/05msy9z54","country_code":"BR","type":"education","lineage":["https://openalex.org/I169248161"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luciano Agostini","raw_affiliation_strings":["Centro de Desenvolvimento Tecnol&#x00F3;gico, Federal University of Pelotas (UFPel), Pelotas, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-3421-5830","affiliations":[{"raw_affiliation_string":"Centro de Desenvolvimento Tecnol&#x00F3;gico, Federal University of Pelotas (UFPel), Pelotas, Brazil","institution_ids":["https://openalex.org/I169248161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I169248161"],"apc_list":null,"apc_paid":null,"fwci":0.4087,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6169212,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"72","issue":"3","first_page":"977","last_page":"988"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9595999717712402,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9595999717712402,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9585000276565552,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T11325","display_name":"Inertial Sensor and Navigation","score":0.9354000091552734,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.6269392967224121},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5928201675415039},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5228524804115295},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39489784836769104},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36364734172821045},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3500116467475891},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32394877076148987},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18823683261871338},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12449035048484802}],"concepts":[{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.6269392967224121},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5928201675415039},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5228524804115295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39489784836769104},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36364734172821045},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3500116467475891},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32394877076148987},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18823683261871338},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12449035048484802},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsi.2024.3486243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2024.3486243","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"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 Circuits and Systems I: Regular Papers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1532362218","https://openalex.org/W1578285471","https://openalex.org/W2082290707","https://openalex.org/W2085261163","https://openalex.org/W2092939357","https://openalex.org/W2793763794","https://openalex.org/W2891639355","https://openalex.org/W2987510390","https://openalex.org/W2997591727","https://openalex.org/W3041140241","https://openalex.org/W3134368609","https://openalex.org/W3152505160","https://openalex.org/W3202918664","https://openalex.org/W4295634057","https://openalex.org/W4385290326","https://openalex.org/W4386598397","https://openalex.org/W4387325069","https://openalex.org/W4395679196","https://openalex.org/W6788339896"],"related_works":["https://openalex.org/W2379084545","https://openalex.org/W2365687337","https://openalex.org/W2358180351","https://openalex.org/W2392368077","https://openalex.org/W2365082216","https://openalex.org/W2388018705","https://openalex.org/W2362703586","https://openalex.org/W2389641749","https://openalex.org/W2386832005","https://openalex.org/W2356919096"],"abstract_inverted_index":{"The":[0],"growing":[1],"consumption":[2],"of":[3,101,170,182,191],"digital":[4],"media,":[5],"driven":[6],"by":[7,12,44,54,106],"technological":[8],"advancements":[9],"and":[10,63,72],"exacerbated":[11],"the":[13,28,33,45,102,108,132,147,153,161,173,189,192,197,201,208],"COVID-19":[14],"pandemic,":[15],"has":[16],"led":[17],"to":[18,91,130,142,145,206],"an":[19,166],"increased":[20],"demand":[21],"for":[22,47,79],"efficient":[23],"video":[24,30],"compression":[25,76],"techniques.":[26],"Among":[27],"various":[29],"encoders":[31],"available,":[32],"AOMedia":[34],"Video":[35],"1":[36],"(AV1)":[37],"stands":[38],"out":[39],"since":[40],"it":[41,73],"was":[42,68,140,158],"defined":[43],"Alliance":[46],"Open":[48],"Media":[49],"(AOMedia),":[50],"which":[51],"is":[52,86,97,128,196],"formed":[53],"big":[55],"techs":[56],"such":[57],"as":[58],"Google,":[59],"Amazon,":[60],"NetFlix,":[61],"Meta,":[62],"Intel,":[64],"among":[65],"others.":[66],"AV1":[67,83,162,209],"launched":[69],"in":[70,165,184,200],"2018":[71],"reaches":[74],"high":[75],"rates,":[77],"especially":[78],"high-resolution":[80],"videos.":[81],"However,":[82],"computational":[84,116,211],"cost":[85],"significantly":[87],"higher":[88],"when":[89],"compared":[90],"other":[92],"current":[93],"codecs.":[94],"This":[95,155],"paper":[96],"focused":[98],"on":[99,179,186],"one":[100],"main":[103],"novelties":[104],"introduced":[105],"AV1:":[107],"Global":[109,122],"Warped":[110,123],"Motion":[111],"Compensation":[112],"(GWMC)":[113],"tool.":[114],"A":[115],"effort":[117],"reduction":[118,169],"approach":[119],"called":[120],"Fast":[121],"(FastGW),":[124],"using":[125],"machine":[126,204],"learning,":[127],"proposed":[129],"reduce":[131,207],"GWMC":[133,210],"processing":[134],"time.":[135],"Then,":[136],"a":[137,176],"decision":[138,156],"tree":[139,157],"trained":[141],"decide":[143],"whether":[144],"skip":[146],"GWMC\u2019s":[148],"most":[149],"computationally":[150],"intensive":[151],"step:":[152],"Refinement.":[154],"implemented":[159],"inside":[160],"encoder,":[163],"resulting":[164],"average":[167],"time":[168],"23%":[171],"at":[172],"GWMC,":[174],"with":[175],"minimal":[177],"impact":[178],"coding":[180],"efficiency":[181],"0.14%":[183],"BD-BR":[185],"average.":[187],"To":[188],"best":[190],"authors\u2019":[193],"knowledge,":[194],"this":[195],"first":[198],"work":[199],"literature":[202],"exploring":[203],"learning":[205],"effort.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
