{"id":"https://openalex.org/W4390479150","doi":"https://doi.org/10.1145/3595916.3626393","title":"Block based Adaptive Compressive Sensing with Sampling Rate Control","display_name":"Block based Adaptive Compressive Sensing with Sampling Rate Control","publication_year":2023,"publication_date":"2023-12-06","ids":{"openalex":"https://openalex.org/W4390479150","doi":"https://doi.org/10.1145/3595916.3626393"},"language":"en","primary_location":{"id":"doi:10.1145/3595916.3626393","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3595916.3626393","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3595916.3626393","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Multimedia Asia 2023","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3595916.3626393","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066564870","display_name":"Kosuke Iwama","orcid":"https://orcid.org/0009-0009-3334-4583"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kosuke Iwama","raw_affiliation_strings":["Hosei University, JP"],"affiliations":[{"raw_affiliation_string":"Hosei University, JP","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008003363","display_name":"Ryugo Morita","orcid":"https://orcid.org/0009-0007-6324-9291"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryugo Morita","raw_affiliation_strings":["Hosei University, JP"],"affiliations":[{"raw_affiliation_string":"Hosei University, JP","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021863826","display_name":"Jinjia Zhou","orcid":"https://orcid.org/0000-0002-5078-0522"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinjia Zhou","raw_affiliation_strings":["Hosei University, JP"],"affiliations":[{"raw_affiliation_string":"Hosei University, JP","institution_ids":["https://openalex.org/I204291657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066564870"],"corresponding_institution_ids":["https://openalex.org/I204291657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2020718,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9979000091552734,"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/computer-science","display_name":"Computer science","score":0.7747828364372253},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.7682421207427979},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7211517691612244},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6538119316101074},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5531483888626099},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5501081943511963},{"id":"https://openalex.org/keywords/nyquist-rate","display_name":"Nyquist rate","score":0.502889096736908},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5016462802886963},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4887857139110565},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.4804239273071289},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4606570899486542},{"id":"https://openalex.org/keywords/motion-compensation","display_name":"Motion compensation","score":0.4424435794353485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.413775771856308},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3936474621295929},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12146848440170288},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0847102701663971}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7747828364372253},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7682421207427979},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7211517691612244},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6538119316101074},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5531483888626099},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5501081943511963},{"id":"https://openalex.org/C65914096","wikidata":"https://www.wikidata.org/wiki/Q6273772","display_name":"Nyquist rate","level":4,"score":0.502889096736908},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5016462802886963},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4887857139110565},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.4804239273071289},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4606570899486542},{"id":"https://openalex.org/C128840427","wikidata":"https://www.wikidata.org/wiki/Q1302174","display_name":"Motion compensation","level":2,"score":0.4424435794353485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.413775771856308},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3936474621295929},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12146848440170288},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0847102701663971},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.0},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3595916.3626393","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3595916.3626393","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3595916.3626393","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Multimedia Asia 2023","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3595916.3626393","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3595916.3626393","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3595916.3626393","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Multimedia Asia 2023","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.49000000953674316}],"awards":[{"id":"https://openalex.org/G1069223013","display_name":null,"funder_award_id":"JSPS KAKENHI","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G1187135940","display_name":null,"funder_award_id":"JP22K12101","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G1254020277","display_name":null,"funder_award_id":"22K12101","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G3459562248","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G4636223006","display_name":null,"funder_award_id":"JSPS KAK","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5506463476","display_name":null,"funder_award_id":"JP22K1","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6535413228","display_name":"\u5341\u516d\u4e16\u7d00\u3092\u4e2d\u5fc3\u3068\u3057\u305f\u757f\u5185\u6751\u843d\u306e\u57fa\u790e\u69cb\u9020","funder_award_id":"12101","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G8430481527","display_name":null,"funder_award_id":"Number","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390479150.pdf","grobid_xml":"https://content.openalex.org/works/W4390479150.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2018990310","https://openalex.org/W2029816571","https://openalex.org/W2101675075","https://openalex.org/W2122548617","https://openalex.org/W2142996775","https://openalex.org/W2172169111","https://openalex.org/W2273561594","https://openalex.org/W2963081547","https://openalex.org/W2970781880","https://openalex.org/W3009991223","https://openalex.org/W3048134800","https://openalex.org/W4226420586","https://openalex.org/W4236965008","https://openalex.org/W4250955649","https://openalex.org/W4292230951"],"related_works":["https://openalex.org/W2889294138","https://openalex.org/W2150066276","https://openalex.org/W4387870235","https://openalex.org/W2046270755","https://openalex.org/W2005032074","https://openalex.org/W2356275480","https://openalex.org/W4250067459","https://openalex.org/W2520516827","https://openalex.org/W3030866127","https://openalex.org/W2938042419"],"abstract_inverted_index":{"Compressive":[0],"sensing":[1,58],"(CS),":[2],"acquiring":[3],"and":[4,16,23,51,83,108,127,144,156,184],"reconstructing":[5],"signals":[6],"below":[7],"the":[8,26,34,39,45,86,97,122,132,136,154,162,165,169],"Nyquist":[9],"rate,":[10],"has":[11],"great":[12],"potential":[13],"in":[14,48],"image":[15],"video":[17,40,49],"acquisition":[18],"to":[19,112,117,140,161,181],"exploit":[20],"data":[21,36],"redundancy":[22,47],"greatly":[24],"reduce":[25,33,141],"amount":[27],"of":[28,71,88,124,153,164],"sampled":[29,35],"data.":[30],"To":[31,67],"further":[32],"while":[37],"keeping":[38],"quality,":[41,147],"this":[42,177],"paper":[43],"explores":[44],"temporal":[46],"CS":[50],"proposes":[52],"a":[53,61,104,109,150],"block":[54,78,105],"based":[55,120],"adaptive":[56,114],"compressive":[57],"framework":[59],"with":[60],"sampling":[62],"rate":[63],"(SR)":[64],"control":[65,182],"strategy.":[66],"avoid":[68],"redundant":[69],"compression":[70],"non-moving":[72,92,157,166],"regions,":[73],"we":[74,102,148],"first":[75],"incorporate":[76],"moving":[77,89,125,155],"detection":[79],"between":[80],"consecutive":[81],"frames,":[82],"only":[84],"transmit":[85],"measurements":[87,163],"blocks.":[90],"The":[91],"regions":[93,126],"are":[94],"reconstructed":[95],"from":[96,168],"previous":[98,170],"frame.":[99,171],"In":[100],"addition,":[101],"propose":[103],"storage":[106],"system":[107],"dynamic":[110],"threshold":[111],"achieve":[113],"SR":[115,129,134,183],"allocation":[116],"each":[118],"frame":[119],"on":[121],"area":[123],"target":[128,137],"for":[130],"controlling":[131],"average":[133],"within":[135],"SR.":[138],"Finally,":[139],"blocking":[142],"artifacts":[143],"improve":[145],"reconstruction":[146,152],"adopt":[149],"cooperative":[151],"blocks":[158,167],"by":[159],"referring":[160],"Extensive":[172],"experiments":[173],"have":[174],"demonstrated":[175],"that":[176],"work":[178],"is":[179],"able":[180],"obtain":[185],"better":[186],"performance":[187],"than":[188],"existing":[189],"works.":[190]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
