{"id":"https://openalex.org/W3118197542","doi":"https://doi.org/10.1109/mmsp48831.2020.9287103","title":"Graph-based skeleton data compression","display_name":"Graph-based skeleton data compression","publication_year":2020,"publication_date":"2020-09-21","ids":{"openalex":"https://openalex.org/W3118197542","doi":"https://doi.org/10.1109/mmsp48831.2020.9287103","mag":"3118197542"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp48831.2020.9287103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp48831.2020.9287103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-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/A5074685765","display_name":"Pratyusha Das","orcid":"https://orcid.org/0000-0002-0398-2949"},"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":"Pratyusha Das","raw_affiliation_strings":["Electrical and Computer Engineering, University of Southern California, Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"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":["Electrical and Computer Engineering, University of Southern California, Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5873,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.70445143,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"945","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","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/T10812","display_name":"Human Pose and Action Recognition","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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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/T11439","display_name":"Video Analysis and Summarization","score":0.998199999332428,"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/lossless-compression","display_name":"Lossless compression","score":0.8154913783073425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7099669575691223},{"id":"https://openalex.org/keywords/entropy-encoding","display_name":"Entropy encoding","score":0.5525665879249573},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5477404594421387},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.5330998301506042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48766863346099854},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44966262578964233},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44157305359840393},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4239111840724945},{"id":"https://openalex.org/keywords/adaptive-coding","display_name":"Adaptive coding","score":0.41247838735580444},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4115177392959595},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3558717370033264},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23612144589424133}],"concepts":[{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.8154913783073425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7099669575691223},{"id":"https://openalex.org/C1769480","wikidata":"https://www.wikidata.org/wiki/Q1345239","display_name":"Entropy encoding","level":3,"score":0.5525665879249573},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5477404594421387},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.5330998301506042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48766863346099854},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44966262578964233},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44157305359840393},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4239111840724945},{"id":"https://openalex.org/C57890076","wikidata":"https://www.wikidata.org/wiki/Q4680725","display_name":"Adaptive coding","level":4,"score":0.41247838735580444},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4115177392959595},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3558717370033264},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23612144589424133},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp48831.2020.9287103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp48831.2020.9287103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W48220658","https://openalex.org/W1913661415","https://openalex.org/W2024171325","https://openalex.org/W2056898157","https://openalex.org/W2120485259","https://openalex.org/W2134905412","https://openalex.org/W2140196014","https://openalex.org/W2161056508","https://openalex.org/W2184544926","https://openalex.org/W2287304320","https://openalex.org/W2297328716","https://openalex.org/W2408775470","https://openalex.org/W2556558971","https://openalex.org/W2559085405","https://openalex.org/W2745471877","https://openalex.org/W2779501054","https://openalex.org/W2810912957","https://openalex.org/W2963432281","https://openalex.org/W2963855530","https://openalex.org/W2964134613","https://openalex.org/W2964228184","https://openalex.org/W2964693622","https://openalex.org/W2967671504","https://openalex.org/W2969163779","https://openalex.org/W2970023392","https://openalex.org/W3001604474","https://openalex.org/W4230938240"],"related_works":["https://openalex.org/W2155738200","https://openalex.org/W2167897635","https://openalex.org/W36148607","https://openalex.org/W2127140080","https://openalex.org/W2128890367","https://openalex.org/W1489137","https://openalex.org/W4253257261","https://openalex.org/W2482609302","https://openalex.org/W1513042817","https://openalex.org/W2164051276"],"abstract_inverted_index":{"With":[0],"the":[1,102,106,110],"advancement":[2],"of":[3,52,105],"reliable,":[4],"fast,":[5],"portable":[6],"acquisition":[7],"systems,":[8],"human":[9],"motion":[10],"capture":[11],"data":[12,54],"is":[13],"becoming":[14],"widely":[15],"used":[16],"in":[17,41],"many":[18],"industrial,":[19],"medical,":[20],"and":[21,44,90],"surveillance":[22],"applications.":[23],"These":[24],"systems":[25],"can":[26],"track":[27],"multiple":[28],"people":[29],"simultaneously,":[30],"providing":[31],"full-body":[32],"skeletal":[33],"keypoints":[34],"as":[35,37],"well":[36],"more":[38],"detailed":[39],"landmarks":[40],"face,":[42],"hands":[43],"feet.":[45],"This":[46],"leads":[47],"to":[48,55],"a":[49,79,118],"huge":[50],"amount":[51],"skeleton":[53],"be":[56],"transmitted":[57],"or":[58],"stored.":[59],"In":[60,128],"this":[61],"paper,":[62],"we":[63],"introduce":[64],"Graph-based":[65],"Skeleton":[66],"Compression":[67],"(GSC),":[68],"an":[69],"efficient":[70],"graph-based":[71],"method":[72,108,116,123],"for":[73,96],"nearly":[74,97],"lossless":[75,98],"compression.":[76,99],"We":[77,100],"use":[78],"separable":[80],"spatio-temporal":[81],"graph":[82],"transform":[83,122],"along":[84,125],"with":[85,93],"non-uniform":[86],"quantization,":[87],"coefficient":[88],"scanning":[89],"entropy":[91],"coding":[92],"run-length":[94],"codes":[95],"evaluate":[101],"compression":[103,133],"performance":[104],"proposed":[107,132],"on":[109],"large":[111],"NTU-RGB":[112],"activity":[113],"dataset.":[114],"Our":[115],"outperforms":[117],"1D":[119],"discrete":[120],"cosine":[121],"applied":[124],"temporal":[126],"direction.":[127],"near-lossless":[129],"mode":[130],"our":[131],"does":[134],"not":[135],"affect":[136],"action":[137],"recognition":[138],"performance.":[139]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
