{"id":"https://openalex.org/W3007511706","doi":"https://doi.org/10.1109/tip.2020.3046872","title":"RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling-Based Video Coding","display_name":"RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling-Based Video Coding","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3007511706","doi":"https://doi.org/10.1109/tip.2020.3046872","mag":"3007511706"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.3046872","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tip.2020.3046872","pdf_url":"https://ieeexplore.ieee.org/ielx7/83/9263394/09318509.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/83/9263394/09318509.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016293039","display_name":"Man M. Ho","orcid":"https://orcid.org/0000-0002-7178-8873"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Man M. Ho","raw_affiliation_strings":["Graduate School of Science and Engineering, Hosei University, Koganei, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Hosei University, Koganei, Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021863826","display_name":"Jinjia Zhou","orcid":"https://orcid.org/0000-0002-5078-0522"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinjia Zhou","raw_affiliation_strings":["Graduate School of Science and Engineering, Hosei University, Koganei, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Hosei University, Koganei, Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052913836","display_name":"Gang He","orcid":"https://orcid.org/0000-0003-0022-4357"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang He","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China","State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016293039"],"corresponding_institution_ids":["https://openalex.org/I204291657"],"apc_list":null,"apc_paid":null,"fwci":3.3985,"has_fulltext":true,"cited_by_count":46,"citation_normalized_percentile":{"value":0.93722734,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"30","issue":null,"first_page":"1702","last_page":"1715"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980000257492065,"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":0.9959999918937683,"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/computer-science","display_name":"Computer science","score":0.809590220451355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5916669964790344},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.529435396194458},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5257447957992554},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5235835313796997},{"id":"https://openalex.org/keywords/compression-ratio","display_name":"Compression ratio","score":0.5067673325538635},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5009520053863525},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4785540699958801},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4568082392215729},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42983436584472656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34546011686325073},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1028924286365509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.809590220451355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5916669964790344},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.529435396194458},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5257447957992554},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5235835313796997},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.5067673325538635},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5009520053863525},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4785540699958801},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4568082392215729},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42983436584472656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34546011686325073},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1028924286365509},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C511840579","wikidata":"https://www.wikidata.org/wiki/Q12757","display_name":"Internal combustion engine","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2020.3046872","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tip.2020.3046872","pdf_url":"https://ieeexplore.ieee.org/ielx7/83/9263394/09318509.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2002.10739","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.10739","pdf_url":"https://arxiv.org/pdf/2002.10739","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":"doi:10.1109/tip.2020.3046872","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tip.2020.3046872","pdf_url":"https://ieeexplore.ieee.org/ielx7/83/9263394/09318509.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G3139095960","display_name":null,"funder_award_id":"JPMJPR1757 Japan","funder_id":"https://openalex.org/F4320334789","funder_display_name":"Japan Science and Technology Agency"},{"id":"https://openalex.org/G3456595407","display_name":null,"funder_award_id":"JPMJPR1757","funder_id":"https://openalex.org/F4320338111","funder_display_name":"Precursory Research for Embryonic Science and Technology"},{"id":"https://openalex.org/G5518604981","display_name":null,"funder_award_id":"PREST","funder_id":"https://openalex.org/F4320338111","funder_display_name":"Precursory Research for Embryonic Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320334789","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19"},{"id":"https://openalex.org/F4320338111","display_name":"Precursory Research for Embryonic Science and Technology","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3007511706.pdf","grobid_xml":"https://content.openalex.org/works/W3007511706.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1885185971","https://openalex.org/W1901129140","https://openalex.org/W2004185490","https://openalex.org/W2161112041","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2461158874","https://openalex.org/W2503339013","https://openalex.org/W2508457857","https://openalex.org/W2549139847","https://openalex.org/W2560023338","https://openalex.org/W2589759987","https://openalex.org/W2607041014","https://openalex.org/W2612063021","https://openalex.org/W2736981815","https://openalex.org/W2747898905","https://openalex.org/W2798722152","https://openalex.org/W2804078698","https://openalex.org/W2866634454","https://openalex.org/W2883102461","https://openalex.org/W2890951410","https://openalex.org/W2894234546","https://openalex.org/W2903027626","https://openalex.org/W2903779395","https://openalex.org/W2949359905","https://openalex.org/W2950893734","https://openalex.org/W2963446712","https://openalex.org/W2963717866","https://openalex.org/W2963741402","https://openalex.org/W2964101377","https://openalex.org/W2964121744","https://openalex.org/W2964277374","https://openalex.org/W2968917279","https://openalex.org/W2993921544","https://openalex.org/W2994689640","https://openalex.org/W2998071001","https://openalex.org/W3032666109","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6752378368","https://openalex.org/W6757108679","https://openalex.org/W6767164110"],"related_works":["https://openalex.org/W4383723869","https://openalex.org/W2161302774","https://openalex.org/W2388481516","https://openalex.org/W4384298135","https://openalex.org/W4383722264","https://openalex.org/W1723410974","https://openalex.org/W3007688875","https://openalex.org/W2766695209","https://openalex.org/W3123970444","https://openalex.org/W2110517301"],"abstract_inverted_index":{"Integrating":[0],"deep":[1,79,171],"learning":[2,138],"techniques":[3,55],"into":[4],"the":[5,14,33,52,67,84,99,126,142,148,155,183,199],"video":[6,25],"coding":[7,26],"framework":[8],"gains":[9],"significant":[10],"improvement":[11],"compared":[12],"to":[13,22,44,50,110,151],"standard":[15,200],"compression":[16,38,92,100],"techniques,":[17],"especially":[18],"applying":[19],"super-resolution":[20,40,68],"(up-sampling)":[21],"down-sampling":[23],"based":[24],"as":[27,116,133],"post-processing.":[28],"However,":[29,58],"besides":[30],"up-sampling":[31,161],"degradation,":[32],"various":[34],"artifacts":[35],"brought":[36],"from":[37,91,141,158],"make":[39],"problem":[41],"more":[42],"difficult":[43],"solve.":[45],"The":[46,202],"straightforward":[47,127],"solution":[48],"is":[49,107,167,205],"integrate":[51],"artifact":[53],"removal":[54],"before":[56],"super-resolution.":[57],"some":[59],"helpful":[60],"features":[61,157],"may":[62],"be":[63],"removed":[64],"together,":[65],"degrading":[66],"performance.":[69],"To":[70],"address":[71],"this":[72],"problem,":[73,145],"we":[74,96,146],"proposed":[75],"an":[76],"end-to-end":[77],"restoration-reconstruction":[78,169],"neural":[80,172],"network":[81,128,149,173],"(RR-DnCNN)":[82],"using":[83,160],"degradation-aware":[85],"technique,":[86],"which":[87],"entirely":[88],"solves":[89],"degradation":[90,101,113],"and":[93,120,186],"sub-sampling.":[94],"Besides,":[95],"proved":[97],"that":[98],"produced":[102],"by":[103,198],"Random":[104],"Access":[105],"configuration":[106],"rich":[108],"enough":[109],"cover":[111],"other":[112],"types,":[114],"such":[115],"Low":[117],"Delay":[118],"P":[119],"All":[121],"Intra,":[122],"for":[123,195],"training.":[124],"Since":[125],"RR-DnCNN":[129,180],"with":[130],"many":[131],"layers":[132],"a":[134,177],"chain":[135],"has":[136],"poor":[137],"capability":[139],"suffering":[140],"gradient":[143],"vanishing":[144],"redesign":[147],"architecture":[150,166],"let":[152],"reconstruction":[153],"leverages":[154],"captured":[156],"restoration":[159],"skip":[162],"connections.":[163],"Our":[164],"novel":[165],"called":[168],"u-shaped":[170],"(RR-DnCNN":[174],"v2.0).":[175],"As":[176],"result,":[178],"our":[179],"v2.0":[181],"outperforms":[182],"previous":[184],"works":[185],"can":[187],"attain":[188],"17.02%":[189],"BD-rate":[190],"reduction":[191],"on":[192],"UHD":[193],"resolution":[194],"all-intra":[196],"anchored":[197],"H.265/HEVC.":[201],"source":[203],"code":[204],"available":[206],"at":[207],"https://minhmanho.github.io/rrdncnn/.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2020-03-06T00:00:00"}