{"id":"https://openalex.org/W2998077213","doi":"https://doi.org/10.1109/tcsvt.2019.2963715","title":"METEOR: Measurable Energy Map Toward the Estimation of Resampling Rate via a Convolutional Neural Network","display_name":"METEOR: Measurable Energy Map Toward the Estimation of Resampling Rate via a Convolutional Neural Network","publication_year":2020,"publication_date":"2020-01-03","ids":{"openalex":"https://openalex.org/W2998077213","doi":"https://doi.org/10.1109/tcsvt.2019.2963715","mag":"2998077213"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2019.2963715","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2019.2963715","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 for Video Technology","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/A5100626866","display_name":"Feng Ding","orcid":"https://orcid.org/0009-0006-2276-189X"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Ding","raw_affiliation_strings":["School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China","Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]},{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049733139","display_name":"Hanzhou Wu","orcid":"https://orcid.org/0000-0002-1599-7232"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzhou Wu","raw_affiliation_strings":["School of Communication and Information Engineering, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Communication and Information Engineering, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091556887","display_name":"Guopu Zhu","orcid":"https://orcid.org/0000-0001-7956-5343"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guopu Zhu","raw_affiliation_strings":["Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111876760","display_name":"Yun-Qing Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yun-Qing Shi","raw_affiliation_strings":["New Jersey Institute of Technology, Newark, NJ, USA"],"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100626866"],"corresponding_institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":2.7355,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.91817907,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"30","issue":"12","first_page":"4715","last_page":"4727"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9779999852180481,"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/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9771000146865845,"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":0.8080698251724243},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.75642991065979},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.6807395219802856},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.672165036201477},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6031543612480164},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4294032156467438},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.418556272983551},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39326873421669006},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3770585060119629},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3366106152534485}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8080698251724243},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.75642991065979},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6807395219802856},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.672165036201477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6031543612480164},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4294032156467438},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.418556272983551},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39326873421669006},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3770585060119629},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3366106152534485},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2019.2963715","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2019.2963715","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8500000238418579}],"awards":[{"id":"https://openalex.org/G2211833943","display_name":null,"funder_award_id":"61902235","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4708300553","display_name":null,"funder_award_id":"61572489","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7809598670","display_name":null,"funder_award_id":"61872350","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W22271197","https://openalex.org/W120119343","https://openalex.org/W147723833","https://openalex.org/W1552869711","https://openalex.org/W1641522613","https://openalex.org/W1825251438","https://openalex.org/W1975528596","https://openalex.org/W1997127867","https://openalex.org/W2009437133","https://openalex.org/W2015914089","https://openalex.org/W2016619935","https://openalex.org/W2038425697","https://openalex.org/W2045719604","https://openalex.org/W2046686077","https://openalex.org/W2050982421","https://openalex.org/W2052389107","https://openalex.org/W2052950171","https://openalex.org/W2055745001","https://openalex.org/W2070484489","https://openalex.org/W2088023168","https://openalex.org/W2088263286","https://openalex.org/W2096045964","https://openalex.org/W2097117768","https://openalex.org/W2099013489","https://openalex.org/W2104657103","https://openalex.org/W2107461197","https://openalex.org/W2117500881","https://openalex.org/W2136313643","https://openalex.org/W2153635508","https://openalex.org/W2155180118","https://openalex.org/W2163470764","https://openalex.org/W2194775991","https://openalex.org/W2412509443","https://openalex.org/W2561315210","https://openalex.org/W2610001287","https://openalex.org/W2618530766","https://openalex.org/W2737157722","https://openalex.org/W2768613569","https://openalex.org/W2794134019","https://openalex.org/W2798117183","https://openalex.org/W2888822137","https://openalex.org/W2902551928","https://openalex.org/W2902617128","https://openalex.org/W2911098213","https://openalex.org/W2919115771","https://openalex.org/W2963777235","https://openalex.org/W2984516443","https://openalex.org/W3021149756","https://openalex.org/W4244098250","https://openalex.org/W4245766349","https://openalex.org/W4255023712","https://openalex.org/W4256068510","https://openalex.org/W4308831279","https://openalex.org/W6604906609","https://openalex.org/W6756444276"],"related_works":["https://openalex.org/W2052515325","https://openalex.org/W2050948537","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"In":[0,58],"recent":[1],"years,":[2],"with":[3,174,189],"the":[4,32,86,95,114,135,139,142,148,151],"improvements":[5],"in":[6,15,25,35,133,161,186],"machine":[7,170],"learning,":[8,171],"image":[9,26,96],"forensics":[10,27,188],"has":[11,167],"made":[12],"considerable":[13],"progress":[14,20],"detecting":[16],"editing":[17],"manipulations.":[18],"This":[19,101],"also":[21,146,178],"raises":[22],"more":[23],"questions":[24],"research,":[28],"such":[29],"as":[30],"can":[31,131,145,177],"parameters":[33],"applied":[34],"a":[36,65,71,75,109],"manipulation":[37],"be":[38,126,179],"estimated.":[39],"Many":[40],"parameter":[41,67,184],"estimation":[42,68,115,136,185],"works":[43,52],"have":[44],"already":[45],"been":[46],"performed.":[47],"However,":[48],"most":[49],"of":[50,93,116,138,150],"these":[51],"are":[53],"based":[54],"on":[55],"mathematical":[56],"analyses.":[57],"this":[59],"paper,":[60],"we":[61],"attempt":[62],"to":[63,84,107,125,181,183],"solve":[64],"particular":[66],"problem":[69],"from":[70],"different":[72],"aspect.":[73],"Specifically,":[74],"new":[76],"convolutional":[77],"neural":[78],"network":[79],"(CNN)":[80],"model":[81,102],"is":[82,97,123],"proposed":[83],"estimate":[85],"resampling":[87,117],"rate":[88,118],"for":[89],"resampled":[90],"images":[91],"regardless":[92],"whether":[94],"upscaled":[98],"or":[99],"downscaled.":[100],"features":[103],"an":[104,127],"original":[105],"layer":[106,122,144],"generate":[108],"measurable":[110],"energy":[111],"map":[112],"toward":[113],"(METEOR).":[119],"The":[120],"METEOR":[121,143],"demonstrated":[124],"outstanding":[128],"method":[129],"that":[130,169],"assist":[132],"enhancing":[134],"performance":[137,191],"CNN.":[140],"Furthermore,":[141],"increase":[147],"robustness":[149],"CNN":[152],"against":[153],"JPEG":[154],"compression,":[155],"which":[156],"makes":[157],"it":[158],"extremely":[159],"important":[160],"realistic":[162],"application":[163],"scenarios.":[164],"Our":[165],"work":[166],"verified":[168],"particularly":[172],"CNNs,":[173],"proper":[175],"optimization":[176],"refined":[180],"adapt":[182],"digital":[187],"excellent":[190],"and":[192],"robustness.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
