{"id":"https://openalex.org/W4308238072","doi":"https://doi.org/10.1109/icip46576.2022.9897653","title":"Hybrid Model-Based / Data-Driven Graph Transform for Image Coding","display_name":"Hybrid Model-Based / Data-Driven Graph Transform for Image Coding","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308238072","doi":"https://doi.org/10.1109/icip46576.2022.9897653"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897653","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897653","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5063893941","display_name":"Saghar Bagheri","orcid":"https://orcid.org/0009-0006-4619-7365"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Saghar Bagheri","raw_affiliation_strings":["York University,Toronto,Canada","York University, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University,Toronto,Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086591736","display_name":"Tam Thuc","orcid":"https://orcid.org/0000-0003-4632-0790"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Tam Thuc Do","raw_affiliation_strings":["York University,Toronto,Canada","York University, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University,Toronto,Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038897476","display_name":"Gene Cheung","orcid":"https://orcid.org/0000-0002-5571-4137"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Gene Cheung","raw_affiliation_strings":["York University,Toronto,Canada","York University, Toronto, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University,Toronto,Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040001106","display_name":"Antonio Ortega","orcid":"https://orcid.org/0000-0001-5403-0940"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonio Ortega","raw_affiliation_strings":["University of Southern California,CA,USA","University of Southern California, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California,CA,USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"University of Southern California, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5004,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64115186,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3667","last_page":"3671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983000159263611,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.6392123103141785},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.581142008304596},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4891562759876251},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.480241596698761},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.442663311958313},{"id":"https://openalex.org/keywords/karhunen\u2013lo\u00e8ve-theorem","display_name":"Karhunen\u2013Lo\u00e8ve theorem","score":0.4388788938522339},{"id":"https://openalex.org/keywords/lapped-transform","display_name":"Lapped transform","score":0.428315132856369},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.37106743454933167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3464251160621643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26558440923690796},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.23472052812576294},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1826431155204773}],"concepts":[{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.6392123103141785},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.581142008304596},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4891562759876251},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.480241596698761},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.442663311958313},{"id":"https://openalex.org/C109308471","wikidata":"https://www.wikidata.org/wiki/Q2046647","display_name":"Karhunen\u2013Lo\u00e8ve theorem","level":2,"score":0.4388788938522339},{"id":"https://openalex.org/C91458471","wikidata":"https://www.wikidata.org/wiki/Q17096468","display_name":"Lapped transform","level":5,"score":0.428315132856369},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.37106743454933167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3464251160621643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26558440923690796},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.23472052812576294},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1826431155204773}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897653","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897653","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W646582900","https://openalex.org/W658512522","https://openalex.org/W1566428659","https://openalex.org/W1980811840","https://openalex.org/W2000769684","https://openalex.org/W2018342351","https://openalex.org/W2026732536","https://openalex.org/W2051142108","https://openalex.org/W2086018247","https://openalex.org/W2105760337","https://openalex.org/W2132555912","https://openalex.org/W2140544897","https://openalex.org/W2160445188","https://openalex.org/W2166816543","https://openalex.org/W2291102274","https://openalex.org/W2615556757","https://openalex.org/W2796431263","https://openalex.org/W2962978500","https://openalex.org/W2971415386","https://openalex.org/W3104309600","https://openalex.org/W3161082783","https://openalex.org/W4210291852","https://openalex.org/W4230036366","https://openalex.org/W4230938240","https://openalex.org/W4241652050"],"related_works":["https://openalex.org/W2258231948","https://openalex.org/W2162505377","https://openalex.org/W2094583657","https://openalex.org/W2111266495","https://openalex.org/W2112852877","https://openalex.org/W2115252864","https://openalex.org/W2106400387","https://openalex.org/W2119239074","https://openalex.org/W2133833176","https://openalex.org/W2953035947"],"abstract_inverted_index":{"Transform":[0],"coding":[1,173],"to":[2,42,53],"sparsify":[3],"signal":[4],"representations":[5],"remains":[6],"crucial":[7],"in":[8,33,134],"an":[9,20,55],"image":[10,41,161],"compression":[11],"pipeline.":[12],"While":[13],"the":[14,67,82,91,130,145],"Karhunen-Lo\u00e8ve":[15],"transform":[16,73,86,104,170,178],"(KLT)":[17],"computed":[18,96],"from":[19,38,77,97],"empirical":[21],"covariance":[22],"matrix":[23,74,119],"${\\mathbf{\\bar":[24,45,98],"C}}$":[25,46,99],"is":[26,106],"theoretically":[27],"optimal":[28],"for":[29,88,100],"a":[30,39,61,72,78,109,116,121,126,135,159],"stationary":[31],"process,":[32],"practice,":[34],"collecting":[35],"sufficient":[36],"statistics":[37],"non-stationary":[40],"reliably":[43],"estimate":[44],"can":[47],"be":[48],"difficult.":[49],"In":[50],"this":[51],"paper,":[52],"encode":[54],"intra-prediction":[56],"residual":[57],"block,":[58],"we":[59,114],"pursue":[60],"hybrid":[62,168],"model-based":[63],"/":[64],"data-driven":[65],"approach:":[66],"first":[68,131],"K":[69,132],"eigenvectors":[70,133],"of":[71,138],"are":[75,95],"derived":[76],"statistical":[79],"model,":[80],"e.g.,":[81],"asymmetric":[83],"discrete":[84,176],"sine":[85],"(ADST),":[87],"stability,":[89],"while":[90],"remaining":[92],"N":[93],"\u2212K":[94],"data":[101],"adaptivity.":[102],"The":[103],"computation":[105],"posed":[107],"as":[108,158],"graph":[110,117,169],"learning":[111],"problem,":[112],"where":[113],"seek":[115],"Laplacian":[118],"minimizing":[120],"graphical":[122],"lasso":[123],"objective":[124],"inside":[125],"convex":[127],"cone":[128],"sharing":[129],"Hilbert":[136],"space":[137],"real":[139],"symmetric":[140],"matrices.":[141],"We":[142],"efficiently":[143],"solve":[144],"problem":[146],"via":[147],"augmented":[148],"Lagrangian":[149],"relaxation":[150],"and":[151,181,183],"proximal":[152],"gradient":[153],"(PG).":[154],"Using":[155],"open-source":[156],"WebP":[157],"baseline":[160],"codec,":[162],"experimental":[163],"results":[164],"show":[165],"that":[166],"our":[167],"achieved":[171],"better":[172,184],"performance":[174],"than":[175,186],"cosine":[177],"(DCT),":[179],"ADST":[180],"KLT,":[182],"stability":[185],"KLT.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
