{"id":"https://openalex.org/W2962906146","doi":"https://doi.org/10.1109/dcc.2019.00127","title":"Deep Multiple Description Coding by Learning Scalar Quantization","display_name":"Deep Multiple Description Coding by Learning Scalar Quantization","publication_year":2019,"publication_date":"2019-03-01","ids":{"openalex":"https://openalex.org/W2962906146","doi":"https://doi.org/10.1109/dcc.2019.00127","mag":"2962906146"},"language":"en","primary_location":{"id":"doi:10.1109/dcc.2019.00127","doi":"https://doi.org/10.1109/dcc.2019.00127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dcc.2019.00127","pdf_url":null,"source":{"id":"https://openalex.org/S4210220829","display_name":"DCC","issn_l":"1068-0314","issn":["1068-0314","2375-0359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Data Compression Conference (DCC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1811.01504","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056401979","display_name":"Lijun Zhao","orcid":"https://orcid.org/0000-0002-2305-1914","observed_orcids":["https://orcid.org/0000-0002-2305-1914"]},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lijun Zhao","raw_affiliation_strings":["Beijing Jiaotong University, Institute of Information Science, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Institute of Information Science, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030238978","display_name":"Huihui Bai","orcid":"https://orcid.org/0000-0002-3879-8957","observed_orcids":["https://orcid.org/0000-0002-3879-8957"]},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huihui Bai","raw_affiliation_strings":["Beijing Jiaotong University, Institute of Information Science, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Institute of Information Science, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003248833","display_name":"Anhong Wang","orcid":"https://orcid.org/0000-0001-5413-0490","observed_orcids":["https://orcid.org/0000-0001-5413-0490"]},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anhong Wang","raw_affiliation_strings":["Taiyuan University of Science and Technology, Institute of Digital Media & Communication, Taiyuan, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan University of Science and Technology, Institute of Digital Media & Communication, Taiyuan, P. R. China","institution_ids":["https://openalex.org/I46305995"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100362745","display_name":"Yao Zhao","orcid":"https://orcid.org/0000-0002-8581-9554","observed_orcids":["https://orcid.org/0000-0002-8581-9554"]},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhao","raw_affiliation_strings":["Beijing Jiaotong University, Institute of Information Science, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Institute of Information Science, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.485,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.72097805,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"615","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9965999722480774,"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":0.9965999722480774,"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":0.002199999988079071,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.00039999998989515007,"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/multiple-description-coding","display_name":"multiple description coding","score":0.9729999899864197},{"id":"https://openalex.org/keywords/scalar-quantization","display_name":"scalar quantization","score":0.7839999794960022},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.6759999990463257},{"id":"https://openalex.org/keywords/entropy-estimation","display_name":"entropy estimation","score":0.7110000252723694},{"id":"https://openalex.org/keywords/self-supervised-learning","display_name":"self-supervised learning","score":0.8420000076293945},{"id":"https://openalex.org/keywords/image-compression","display_name":"image compression","score":0.8360000252723694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6780016422271729},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6625335812568665},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5859768986701965},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.558351457118988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5511403679847717},{"id":"https://openalex.org/C135554599","wikidata":"https://www.wikidata.org/wiki/Q3682206","display_name":"Multiple description coding","level":3,"score":0.5403777360916138},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4744887351989746},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.45073947310447693},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4459275007247925},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.42678162455558777},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.42480435967445374},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4168907701969147},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37683093547821045},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20603373646736145},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/dcc.2019.00127","doi":"https://doi.org/10.1109/dcc.2019.00127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dcc.2019.00127","pdf_url":null,"source":{"id":"https://openalex.org/S4210220829","display_name":"DCC","issn_l":"1068-0314","issn":["1068-0314","2375-0359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Data Compression Conference (DCC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1811.01504","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1811.01504","pdf_url":"https://arxiv.org/pdf/1811.01504","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,"listed_in":[],"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"},{"id":"doi:10.48550/arxiv.1811.01504","doi":"https://doi.org/10.48550/arxiv.1811.01504","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1811.01504","pdf_url":null,"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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1811.01504","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1811.01504","pdf_url":"https://arxiv.org/pdf/1811.01504","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,"listed_in":[],"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":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2962906146.pdf","grobid_xml":"https://content.openalex.org/works/W2962906146.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1957253214","https://openalex.org/W2033608874","https://openalex.org/W2086467493","https://openalex.org/W2096102601","https://openalex.org/W2132330780","https://openalex.org/W2136098605","https://openalex.org/W2144916011","https://openalex.org/W2170868882","https://openalex.org/W2286929393","https://openalex.org/W2395310358","https://openalex.org/W2592939477","https://openalex.org/W2604231067","https://openalex.org/W2614571185","https://openalex.org/W2768814045","https://openalex.org/W2782637256","https://openalex.org/W2790037810","https://openalex.org/W2963633319"],"related_works":["https://openalex.org/W2999778088","https://openalex.org/W4214674424","https://openalex.org/W2917598233","https://openalex.org/W3022006356","https://openalex.org/W2132224136","https://openalex.org/W2168052915","https://openalex.org/W4389474600","https://openalex.org/W2536923243","https://openalex.org/W2075049265","https://openalex.org/W4249455378"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,79],"deep":[6],"multiple":[7,20,34,41,68,98,107,116,130,151],"description":[8,21,35,42,69,99,108,117,131,152],"coding":[9,153,158],"framework,":[10],"whose":[11],"quantizers":[12,83],"are":[13,50],"adaptively":[14],"learned":[15,51,91],"via":[16],"the":[17,54,58,65,74],"minimization":[18],"of":[19,57,67,81,157],"compressive":[22],"loss.":[23,133],"Firstly,":[24],"our":[25,145],"framework":[26],"is":[27,89,104,142,147],"built":[28],"upon":[29],"auto-encoder":[30],"networks,":[31],"which":[32,61],"have":[33],"multi-scale":[36],"dilated":[37],"encoder":[38,70],"network":[39,71],"and":[40],"decoder":[43],"networks.":[44],"Secondly,":[45],"two":[46,86,137],"entropy":[47],"estimation":[48],"networks":[49],"to":[52,72,129],"estimate":[53],"informative":[55],"amounts":[56],"quantized":[59],"tensors,":[60],"can":[62],"further":[63],"supervise":[64],"learning":[66],"represent":[73],"input":[75],"image":[76],"delicately.":[77],"Thirdly,":[78],"pair":[80],"scalar":[82],"accompanied":[84],"by":[85],"importance-indicator":[87],"maps":[88],"automatically":[90],"in":[92,111,124,127,155],"an":[93],"end-to-end":[94],"self-supervised":[95],"way.":[96],"Finally,":[97],"structural":[100],"dissimilarity":[101],"distance":[102],"loss":[103],"imposed":[105],"on":[106,121,136],"decoded":[109],"images":[110],"pixel":[112],"domain":[113],"for":[114],"diversified":[115],"generations":[118],"rather":[119],"than":[120],"feature":[122,125],"tensors":[123],"domain,":[126],"addition":[128],"reconstruction":[132],"Through":[134],"testing":[135],"commonly":[138],"used":[139],"datasets,":[140],"it":[141],"verified":[143],"that":[144],"method":[146],"beyond":[148],"several":[149],"state-of-the-art":[150],"approaches":[154],"terms":[156],"efficiency.":[159]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2019-07-30T00:00:00"}