{"id":"https://openalex.org/W2970340508","doi":"https://doi.org/10.1109/tmm.2019.2938345","title":"Energy Compaction-Based Image Compression Using Convolutional AutoEncoder","display_name":"Energy Compaction-Based Image Compression Using Convolutional AutoEncoder","publication_year":2019,"publication_date":"2019-08-29","ids":{"openalex":"https://openalex.org/W2970340508","doi":"https://doi.org/10.1109/tmm.2019.2938345","mag":"2970340508"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2019.2938345","doi":"https://doi.org/10.1109/tmm.2019.2938345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2019.2938345","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,"listed_in":["ccf-a","cwts-core","dongbi-a","jufo-3","norway-1","poland-140","russia-white-list-1","vabb-shw"],"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"},"volume":null,"type":"article","is_traditional":true,"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/A5071945287","display_name":"Zhengxue Cheng","orcid":"https://orcid.org/0000-0001-8258-6364","observed_orcids":["https://orcid.org/0000-0001-8258-6364"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhengxue Cheng","raw_affiliation_strings":["Department of Computer Science and Communication Engineering, Graduate School of Fundamental Science and Engineering, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8258-6364","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Communication Engineering, Graduate School of Fundamental Science and Engineering, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089205282","display_name":"Heming Sun","orcid":"https://orcid.org/0000-0001-5583-4895","observed_orcids":["https://orcid.org/0000-0001-5583-4895"]},"institutions":[{"id":"https://openalex.org/I4210086780","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19","country_code":"JP","type":"government","lineage":["https://openalex.org/I1319490839","https://openalex.org/I4210086780"]},{"id":"https://openalex.org/I4405285833","display_name":"Waseda Research Institute for Science and Engineering","ror":"https://ror.org/00dqvpt71","country_code":"JP","type":"facility","lineage":["https://openalex.org/I150744194","https://openalex.org/I4405285833"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Heming Sun","raw_affiliation_strings":["JST, PRESTO, Kawaguchi, Japan","Waseda Research Institute for Science and Engineering, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-5583-4895","affiliations":[{"raw_affiliation_string":"JST, PRESTO, Kawaguchi, Japan","institution_ids":["https://openalex.org/I4210086780"]},{"raw_affiliation_string":"Waseda Research Institute for Science and Engineering, Tokyo, Japan","institution_ids":["https://openalex.org/I4405285833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045478397","display_name":"Masaru Takeuchi","orcid":"https://orcid.org/0000-0001-8953-0697","observed_orcids":["https://orcid.org/0000-0001-8953-0697"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masaru Takeuchi","raw_affiliation_strings":["Department of Computer Science and Communication Engineering, Graduate School of Fundamental Science and Engineering, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8953-0697","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Communication Engineering, Graduate School of Fundamental Science and Engineering, Tokyo, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002294739","display_name":"Jiro Katto","orcid":"https://orcid.org/0000-0002-1671-2614","observed_orcids":["https://orcid.org/0000-0002-1671-2614"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiro Katto","raw_affiliation_strings":["Department of Computer Science and Communication Engineering, Graduate School of Fundamental Science and Engineering, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-1671-2614","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Communication Engineering, Graduate School of Fundamental Science and Engineering, Tokyo, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":4.7227,"has_fulltext":false,"cited_by_count":103,"citation_normalized_percentile":{"value":0.96182906,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":"22","issue":"4","first_page":"860","last_page":"873"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9955999851226807,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9955999851226807,"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":0.0038999998942017555,"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/T10036","display_name":"Advanced Neural Network Applications","score":9.999999747378752e-05,"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/image-compression","display_name":"image compression","score":0.9470000267028809},{"id":"https://openalex.org/keywords/convolutional-autoencoder","display_name":"convolutional autoencoder","score":0.9620000123977661},{"id":"https://openalex.org/keywords/energy-compaction","display_name":"energy compaction","score":0.996999979019165},{"id":"https://openalex.org/keywords/coding-gain","display_name":"coding gain","score":0.7540000081062317},{"id":"https://openalex.org/keywords/bit-allocation","display_name":"bit allocation","score":0.9350000023841858},{"id":"https://openalex.org/keywords/neural-network-compression","display_name":"neural network compression","score":0.47999998927116394},{"id":"https://openalex.org/keywords/coding-efficiency","display_name":"coding efficiency","score":0.5490000247955322},{"id":"https://openalex.org/keywords/ms-ssim","display_name":"MS-SSIM","score":0.6309999823570251}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061741590499878},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7903241515159607},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.6980072855949402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5414876937866211},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5404887795448303},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5005416870117188},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4764840304851532},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4517216384410858},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.426950067281723},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.41939181089401245},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.40990135073661804},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3290010690689087},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2247622013092041},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16038814187049866},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08238795399665833},{"id":"https://openalex.org/C511840579","wikidata":"https://www.wikidata.org/wiki/Q12757","display_name":"Internal combustion engine","level":2,"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2019.2938345","doi":"https://doi.org/10.1109/tmm.2019.2938345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2019.2938345","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,"listed_in":["ccf-a","cwts-core","dongbi-a","jufo-3","norway-1","poland-140","russia-white-list-1","vabb-shw"],"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"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1670297220","display_name":null,"funder_award_id":"2019Q-049","funder_id":"https://openalex.org/F4320322638","funder_display_name":"Waseda University"},{"id":"https://openalex.org/G3394781526","display_name":"\u6df1\u5c64\u5b66\u7fd2\u306b\u3088\u308b\u4e3b\u89b3\u7684\u9ad8\u753b\u8cea\u5316\u3092\u76ee\u6307\u3057\u305f\u65b0\u3057\u3044\u5727\u7e2e\u6280\u8853\u306e\u7814\u7a76","funder_award_id":"19J14620","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G4378001355","display_name":null,"funder_award_id":"JPMJPR19M5","funder_id":"https://openalex.org/F4320338111","funder_display_name":"Precursory Research for Embryonic Science and Technology"},{"id":"https://openalex.org/G5917683739","display_name":null,"funder_award_id":"201914620","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320322638","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320338111","display_name":"Precursory Research for Embryonic Science and Technology","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1580389772","https://openalex.org/W1995875735","https://openalex.org/W2011322602","https://openalex.org/W2025768430","https://openalex.org/W2030265202","https://openalex.org/W2040903332","https://openalex.org/W2048916503","https://openalex.org/W2108598243","https://openalex.org/W2140196014","https://openalex.org/W2146395539","https://openalex.org/W2186435531","https://openalex.org/W2194775991","https://openalex.org/W2276024283","https://openalex.org/W2476548250","https://openalex.org/W2516038988","https://openalex.org/W2552465432","https://openalex.org/W2593493485","https://openalex.org/W2597747080","https://openalex.org/W2604392022","https://openalex.org/W2616159609","https://openalex.org/W2729969528","https://openalex.org/W2732044853","https://openalex.org/W2751699067","https://openalex.org/W2753546716","https://openalex.org/W2782637256","https://openalex.org/W2795709826","https://openalex.org/W2798309714","https://openalex.org/W2810785187","https://openalex.org/W2891810238","https://openalex.org/W2894983512","https://openalex.org/W2950237263","https://openalex.org/W2962790638","https://openalex.org/W2962949190","https://openalex.org/W2963419712","https://openalex.org/W2964121744","https://openalex.org/W2970999611","https://openalex.org/W6734035190","https://openalex.org/W6755313046"],"related_works":["https://openalex.org/W4285184703","https://openalex.org/W2798309714","https://openalex.org/W4388287031","https://openalex.org/W2948654165","https://openalex.org/W3008871478","https://openalex.org/W3005064106","https://openalex.org/W2926689860","https://openalex.org/W4317892847","https://openalex.org/W2912766784","https://openalex.org/W4414406030"],"abstract_inverted_index":{"Image":[0],"compression":[1,28,42,68,114,154,194],"has":[2,14,29],"been":[3,31],"an":[4,38,125],"important":[5],"research":[6],"topic":[7],"for":[8,66,77],"many":[9,19],"decades.":[10],"Recently,":[11],"deep":[12],"learning":[13,193],"achieved":[15],"great":[16],"success":[17],"in":[18,26,104,166,178],"computer":[20],"vision":[21],"tasks,":[22],"and":[23,91,151,185],"its":[24],"use":[25],"image":[27,41,67],"gradually":[30],"increasing.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36,61,81,92,123,174],"present":[37],"energy":[39,88,126],"compaction-based":[40,127],"architecture":[43,65],"using":[44],"a":[45,63,83,99,111,133],"convolutional":[46],"autoencoder":[47],"(CAE)":[48],"to":[49,97,135,143],"achieve":[50,152,175],"high":[51,153,197],"coding":[52,101,120,149,188],"efficiency.":[53,155],"Our":[54],"main":[55],"contributions":[56],"include":[57],"three":[58],"aspects:":[59],"1)":[60],"propose":[62,98,124],"CAE":[64,79,146],"by":[69],"decomposing":[70],"it":[71],"into":[72],"several":[73],"down(up)sampling":[74],"operations;":[75],"2)":[76],"our":[78,160],"architecture,":[80],"offer":[82],"mathematical":[84],"analysis":[85],"on":[86,118],"the":[87,94,119,136,140,145,148,169],"compaction":[89],"property":[90],"are":[93],"first":[95],"work":[96],"normalized":[100],"gain":[102,121,150],"metric":[103],"neural":[105],"networks,":[106],"which":[107,131],"can":[108],"act":[109],"as":[110],"measurement":[112],"of":[113,168],"capability;":[115],"3)":[116],"based":[117],"metric,":[122],"bit":[128,182,198],"allocation":[129,183],"method,":[130],"adds":[132],"regularizer":[134],"loss":[137],"function":[138],"during":[139],"training":[141],"stage":[142],"help":[144],"maximize":[147],"The":[156],"experimental":[157],"results":[158],"demonstrate":[159],"proposed":[161],"method":[162],"outperforms":[163],"BPG":[164],"(HEVC-intra),":[165],"terms":[167],"MS-SSIM":[170],"quality":[171],"metric.":[172],"Additionally,":[173],"better":[176],"performance":[177],"comparison":[179],"with":[180,191],"existing":[181],"methods,":[184],"provide":[186],"higher":[187],"efficiency":[189],"compared":[190],"state-of-the-art":[192],"methods":[195],"at":[196],"rates.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2019-09-05T00:00:00"}
