{"id":"https://openalex.org/W2614571185","doi":"https://doi.org/10.1109/tcomm.2017.2704585","title":"On the Design of Symmetric Entropy-constrained Multiple Description Scalar Quantizer with Linear Joint Decoders","display_name":"On the Design of Symmetric Entropy-constrained Multiple Description Scalar Quantizer with Linear Joint Decoders","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2614571185","doi":"https://doi.org/10.1109/tcomm.2017.2704585","mag":"2614571185"},"language":"en","primary_location":{"id":"doi:10.1109/tcomm.2017.2704585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcomm.2017.2704585","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","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 Communications","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/A5005409439","display_name":"Huihui Wu","orcid":"https://orcid.org/0000-0002-1097-3792"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"HUIHUI WU","raw_affiliation_strings":["Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091082655","display_name":"Ting Zheng","orcid":"https://orcid.org/0000-0002-1841-9155"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]},{"id":"https://openalex.org/I4210126794","display_name":"IBM Research (China)","ror":"https://ror.org/02yg1pf55","country_code":"CN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126794"]}],"countries":["CA","CN"],"is_corresponding":false,"raw_author_name":"Ting Zheng","raw_affiliation_strings":["Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada","IBM China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada","institution_ids":["https://openalex.org/I98251732"]},{"raw_affiliation_string":"IBM China, Beijing, China","institution_ids":["https://openalex.org/I4210126794"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070675586","display_name":"Sorina Dumitrescu","orcid":"https://orcid.org/0000-0001-7287-0310"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sorina Dumitrescu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada","institution_ids":["https://openalex.org/I98251732"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005409439"],"corresponding_institution_ids":["https://openalex.org/I98251732"],"apc_list":null,"apc_paid":null,"fwci":0.3641,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67738065,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression 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/T10901","display_name":"Advanced Data Compression 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/T11269","display_name":"Algorithms and Data Compression","score":0.9736999869346619,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11034","display_name":"Digital Filter Design and Implementation","score":0.968999981880188,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5790078043937683},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5261373519897461},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4931444525718689},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.49039649963378906},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.48117193579673767},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.43112102150917053},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4212811589241028},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.414631724357605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37426304817199707}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5790078043937683},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5261373519897461},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4931444525718689},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.49039649963378906},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.48117193579673767},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.43112102150917053},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4212811589241028},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.414631724357605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37426304817199707},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcomm.2017.2704585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcomm.2017.2704585","pdf_url":null,"source":{"id":"https://openalex.org/S196647941","display_name":"IEEE Transactions on Communications","issn_l":"0090-6778","issn":["0090-6778","1558-0857"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","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 Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W7376621","https://openalex.org/W585133165","https://openalex.org/W1569287257","https://openalex.org/W1647020043","https://openalex.org/W1995345730","https://openalex.org/W2019936054","https://openalex.org/W2021995874","https://openalex.org/W2023267926","https://openalex.org/W2024179661","https://openalex.org/W2030339236","https://openalex.org/W2056651359","https://openalex.org/W2069728085","https://openalex.org/W2074931698","https://openalex.org/W2088433481","https://openalex.org/W2096102601","https://openalex.org/W2098818807","https://openalex.org/W2103904364","https://openalex.org/W2105904214","https://openalex.org/W2106159865","https://openalex.org/W2107378894","https://openalex.org/W2117492831","https://openalex.org/W2119561941","https://openalex.org/W2119936328","https://openalex.org/W2125808188","https://openalex.org/W2129344187","https://openalex.org/W2131578006","https://openalex.org/W2134638697","https://openalex.org/W2136098605","https://openalex.org/W2140942991","https://openalex.org/W2148037789","https://openalex.org/W2149567218","https://openalex.org/W2154239264","https://openalex.org/W2158120001","https://openalex.org/W2159083437","https://openalex.org/W2165305114","https://openalex.org/W2168256033","https://openalex.org/W2172163894","https://openalex.org/W2243644693","https://openalex.org/W2296319761","https://openalex.org/W2478708596","https://openalex.org/W2497777345","https://openalex.org/W2540487576","https://openalex.org/W4250589301","https://openalex.org/W4291236916"],"related_works":["https://openalex.org/W3203142394","https://openalex.org/W4390516098","https://openalex.org/W2161474341","https://openalex.org/W1539956819","https://openalex.org/W4302615923","https://openalex.org/W2181948922","https://openalex.org/W2093152993","https://openalex.org/W1974101135","https://openalex.org/W2121067345","https://openalex.org/W3209251257"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,21,24,28,34,40,44,51,63,71,76,85,105,108,111,121,129,143,146,157,166,170,175,182,190,193,205,222,254,267],"design":[4,59,87,195],"of":[5,20,30,36,50,70,75,110,145,159,169,192],"symmetric":[6],"entropy-constrained":[7],"multiple":[8,197,206],"description":[9,198,207],"scalar":[10],"quantizers":[11,201],"(EC-MDSQ)":[12],"with":[13,196,204,227,244,262],"linear":[14,37,176],"joint":[15,177],"decoders,":[16],"i.e.,":[17],"where":[18],"some":[19],"decoders":[22,38,178],"compute":[23],"reconstruction":[25,112],"by":[26,84,90,174],"averaging":[27],"reconstructions":[29],"individual":[31],"descriptions.":[32,102],"Thus,":[33],"use":[35],"reduces":[39],"space":[41],"complexity":[42,184],"at":[43,120,185],"decoder":[45,122],"since":[46],"only":[47],"a":[48,67,132],"subset":[49],"codebooks":[52,236],"needs":[53],"to":[54,100,152,164,179,260],"be":[55,117,139],"stored.":[56],"The":[57,80],"proposed":[58,89,194,223,255],"algorithm":[60,81,88],"locally":[61],"minimizes":[62],"Lagrangian,":[64],"which":[65,137],"is":[66,82,96,131,257],"weighted":[68],"sum":[69],"expected":[72],"distortion":[73],"and":[74,92,94,203,213],"side":[77],"quantizers'":[78],"rates.":[79],"inspired":[83],"EC-MDSQ":[86],"Vaishampayan":[91],"Domaszewicz,":[93],"it":[95],"adapted":[97],"from":[98,104,150],"two":[99,151],"K":[101,153],"Differently":[103],"aforementioned":[106],"work,":[107],"optimization":[109,123,135,148],"values":[113],"can":[114,138],"no":[115],"longer":[116],"performed":[118],"separately":[119],"step.":[124,187],"Interestingly,":[125],"we":[126],"show":[127,162,220],"that":[128,221],"problem":[130],"convex":[133],"quadratic":[134],"problem,":[136],"efficiently":[140],"solved.":[141],"Moreover,":[142],"generalization":[144],"encoder":[147],"step":[149],"descriptions":[154],"increases":[155],"drastically":[156],"amount":[158],"computations.":[160],"We":[161,188],"how":[163],"exploit":[165],"special":[167],"form":[168],"cost":[171],"function":[172],"conferred":[173],"significantly":[180],"reduce":[181],"time":[183],"this":[186],"compare":[189],"performance":[191],"lattice":[199],"vector":[200],"(MDLVQ)":[202],"scheme":[208,240],"based":[209],"on":[210],"successive":[211],"refinement":[212],"unequal":[214],"erasure":[215],"protection":[216],"(UEP).":[217],"Our":[218],"experiments":[219],"approach":[224,256],"outperforms":[225],"MDLVQ":[226,243],"dimension":[228,246,263],"1":[229,264],"quantization,":[230],"as":[231],"expected.":[232],"Additionally,":[233],"when":[234,266],"more":[235],"are":[237,269],"added":[238],"our":[239],"even":[241],"beats":[242],"quantization":[245,265],"approaching":[247],"\u221e,":[248],"for":[249],"rates":[250,268],"sufficiently":[251],"high.":[252],"Furthermore,":[253],"also":[258],"superior":[259],"UEP":[261],"low.":[270]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
