{"id":"https://openalex.org/W2970008269","doi":"https://doi.org/10.1109/icip.2019.8803624","title":"Effect of Architectures and Training Methods on the Performance of Learned Video Frame Prediction","display_name":"Effect of Architectures and Training Methods on the Performance of Learned Video Frame Prediction","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970008269","doi":"https://doi.org/10.1109/icip.2019.8803624","mag":"2970008269"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803624","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803624","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.06106","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103009416","display_name":"M. Ak\u0131n Y\u0131lmaz","orcid":"https://orcid.org/0000-0002-1551-7980"},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"M. Akin Yilmaz","raw_affiliation_strings":["Koc University, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Koc University, Istanbul, Turkey","institution_ids":["https://openalex.org/I1351752"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034648478","display_name":"A. Murat Tekalp","orcid":"https://orcid.org/0000-0003-1465-8121"},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"A. Murat Tekalp","raw_affiliation_strings":["Koc University, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Koc University, Istanbul, Turkey","institution_ids":["https://openalex.org/I1351752"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1351752"],"apc_list":null,"apc_paid":null,"fwci":0.8235,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81621125,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"4","issue":null,"first_page":"4210","last_page":"4214"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","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/T11105","display_name":"Advanced Image Processing Techniques","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/T10531","display_name":"Advanced Vision and Imaging","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/T11019","display_name":"Image Enhancement Techniques","score":0.9988999962806702,"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/stateful-firewall","display_name":"Stateful firewall","score":0.8575977087020874},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8515211939811707},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7534070014953613},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7254036068916321},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.667077362537384},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5448621511459351},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49718526005744934},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4911632239818573},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44282662868499756},{"id":"https://openalex.org/keywords/residual-frame","display_name":"Residual frame","score":0.44178682565689087},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4369368851184845},{"id":"https://openalex.org/keywords/stateless-protocol","display_name":"Stateless protocol","score":0.4180851876735687},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34592610597610474},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32678067684173584},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.31251853704452515},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08807167410850525}],"concepts":[{"id":"https://openalex.org/C22927095","wikidata":"https://www.wikidata.org/wiki/Q1784206","display_name":"Stateful firewall","level":3,"score":0.8575977087020874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8515211939811707},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7534070014953613},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7254036068916321},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.667077362537384},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5448621511459351},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49718526005744934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4911632239818573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44282662868499756},{"id":"https://openalex.org/C204641915","wikidata":"https://www.wikidata.org/wiki/Q7315509","display_name":"Residual frame","level":4,"score":0.44178682565689087},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4369368851184845},{"id":"https://openalex.org/C103613024","wikidata":"https://www.wikidata.org/wiki/Q230924","display_name":"Stateless protocol","level":3,"score":0.4180851876735687},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34592610597610474},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32678067684173584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31251853704452515},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08807167410850525},{"id":"https://openalex.org/C172849965","wikidata":"https://www.wikidata.org/wiki/Q3148875","display_name":"Reference frame","level":3,"score":0.0},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.0},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip.2019.8803624","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803624","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2008.06106","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.06106","pdf_url":"https://arxiv.org/pdf/2008.06106","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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:2008.06106","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.06106","pdf_url":"https://arxiv.org/pdf/2008.06106","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1485009520","https://openalex.org/W1522301498","https://openalex.org/W1815076433","https://openalex.org/W2116435618","https://openalex.org/W2274287116","https://openalex.org/W2476548250","https://openalex.org/W2568597297","https://openalex.org/W2583901669","https://openalex.org/W2607738331","https://openalex.org/W2738136547","https://openalex.org/W2739757502","https://openalex.org/W2765363933","https://openalex.org/W2788033868","https://openalex.org/W2796303840","https://openalex.org/W2808493349","https://openalex.org/W2883615717","https://openalex.org/W2902991992","https://openalex.org/W2952453038","https://openalex.org/W2953318193","https://openalex.org/W2963125871","https://openalex.org/W2963372104","https://openalex.org/W2963402657","https://openalex.org/W2963435596","https://openalex.org/W2963629403","https://openalex.org/W2964121744","https://openalex.org/W2964262118","https://openalex.org/W2964327849","https://openalex.org/W2964350391","https://openalex.org/W4294037149","https://openalex.org/W4297772798","https://openalex.org/W4298076649","https://openalex.org/W4298157138","https://openalex.org/W4298157202","https://openalex.org/W4300428005","https://openalex.org/W6600983433","https://openalex.org/W6631190155","https://openalex.org/W6638545294","https://openalex.org/W6677326919","https://openalex.org/W6691096134","https://openalex.org/W6694260854","https://openalex.org/W6712884540","https://openalex.org/W6728805014","https://openalex.org/W6731654480","https://openalex.org/W6735992252","https://openalex.org/W6745420753","https://openalex.org/W6748392304","https://openalex.org/W6750642828","https://openalex.org/W6752643364","https://openalex.org/W6756605901"],"related_works":["https://openalex.org/W2035312053","https://openalex.org/W2908539414","https://openalex.org/W112987992","https://openalex.org/W4400727979","https://openalex.org/W4246017188","https://openalex.org/W17249245","https://openalex.org/W4399010298","https://openalex.org/W2234619324","https://openalex.org/W4298846198","https://openalex.org/W358240276"],"abstract_inverted_index":{"We":[0,53],"analyze":[1],"the":[2,49,67,72,84,104],"performance":[3],"of":[4,76,86,116],"feedforward":[5],"vs.":[6],"recurrent":[7,61],"neural":[8,29],"network":[9,30,43],"(RNN)":[10],"architectures":[11],"and":[12,36,57,89,100,111],"associated":[13],"training":[14,59,88],"methods":[15],"for":[16,44,60],"learned":[17],"frame":[18,46,125],"prediction.":[19],"To":[20],"this":[21],"effect,":[22],"we":[23],"trained":[24,98],"a":[25,32,37],"residual":[26,68],"fully":[27],"convolutional":[28,33,38],"(FCNN),":[31],"RNN":[34],"(CRNN),":[35],"long":[39],"short-term":[40],"memory":[41],"(CLSTM)":[42],"next":[45],"prediction":[47,126],"using":[48,103],"mean":[50],"square":[51],"loss.":[52],"performed":[54],"both":[55],"stateless":[56],"stateful":[58,105],"networks.":[62],"Experimental":[63],"results":[64],"show":[65],"that":[66],"FCNN":[69],"architecture":[70],"performs":[71],"best":[73],"in":[74],"terms":[75],"peak":[77],"signal":[78],"to":[79,121],"noise":[80],"ratio":[81],"(PSNR)":[82],"at":[83],"expense":[85],"higher":[87],"test":[90],"(inference)":[91],"computational":[92],"complexity.":[93],"The":[94],"CRNN":[95],"can":[96],"be":[97],"stably":[99],"very":[101],"efficiently":[102],"truncated":[106],"backpropagation":[107],"through":[108],"time":[109],"procedure,":[110],"it":[112],"requires":[113],"an":[114,128],"order":[115],"magnitude":[117],"less":[118],"inference":[119],"runtime":[120],"achieve":[122],"near":[123],"real-time":[124],"with":[127],"acceptable":[129],"performance.":[130]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}