{"id":"https://openalex.org/W3116617544","doi":"https://doi.org/10.1109/mmsp48831.2020.9287074","title":"Bi-directional intra prediction based measurement coding for compressive sensing images","display_name":"Bi-directional intra prediction based measurement coding for compressive sensing images","publication_year":2020,"publication_date":"2020-09-21","ids":{"openalex":"https://openalex.org/W3116617544","doi":"https://doi.org/10.1109/mmsp48831.2020.9287074","mag":"3116617544"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp48831.2020.9287074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5102795629","display_name":"Thuy Thi Thu Tran","orcid":"https://orcid.org/0000-0002-5176-8031"},"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":"Thuy T. T. Tran","raw_affiliation_strings":["Graduate School of Science and Engineering, Hosei University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Hosei University, Tokyo, Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048162925","display_name":"Jirayu Peetakul","orcid":"https://orcid.org/0000-0001-8030-3009"},"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":"Jirayu Peetakul","raw_affiliation_strings":["Graduate School of Science and Engineering, Hosei University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Hosei University, Tokyo, Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089218009","display_name":"Chi Do-Kim Pham","orcid":"https://orcid.org/0000-0003-0912-9110"},"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":"Chi D. K. Pham","raw_affiliation_strings":["Graduate School of Science and Engineering, Hosei University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Hosei University, Tokyo, Japan","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":["Graduate School of Science and Engineering, Hosei University, Tokyo, Japan","JST, PRESTO, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Hosei University, Tokyo, Japan","institution_ids":["https://openalex.org/I204291657"]},{"raw_affiliation_string":"JST, PRESTO, Tokyo, Japan","institution_ids":[]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.849,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.924833,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":81,"max":82},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9986,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9985,"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":[],"concepts":[{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.6869647},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.60738164},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.58880347},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5815359},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5675696},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.54268366},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5158436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37695324},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33275577},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25245443},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.1339877},{"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp48831.2020.9287074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":15,"referenced_works":["https://openalex.org/W1559556600","https://openalex.org/W1791560514","https://openalex.org/W2045518970","https://openalex.org/W2047920195","https://openalex.org/W2296616510","https://openalex.org/W2757097699","https://openalex.org/W2799561125","https://openalex.org/W2803670972","https://openalex.org/W2903475965","https://openalex.org/W2945471875","https://openalex.org/W2959530288","https://openalex.org/W3105962908","https://openalex.org/W3142290276","https://openalex.org/W4250955649","https://openalex.org/W4377561911"],"related_works":["https://openalex.org/W4300044672","https://openalex.org/W2810730439","https://openalex.org/W2465351041","https://openalex.org/W2379589510","https://openalex.org/W2378166785","https://openalex.org/W2358292267","https://openalex.org/W2158224665","https://openalex.org/W2135584473","https://openalex.org/W1964277756","https://openalex.org/W1881631164"],"abstract_inverted_index":{"This":[0],"work":[1,116],"proposes":[2],"a":[3],"bi-directional":[4,92],"intra":[5,93],"prediction-based":[6],"measurement":[7,57,87],"coding":[8],"algorithm":[9],"for":[10],"compressive":[11],"sensing":[12,15],"images.":[13],"Compressive":[14],"is":[16],"capable":[17],"of":[18,22,56,76,85,128],"reducing":[19],"the":[20,23,28,34,42,47,54,60,73,83,86,105,125,137],"size":[21],"sparse":[24],"signals,":[25],"in":[26,45],"which":[27,70],"high-dimensional":[29],"signals":[30],"are":[31,80],"represented":[32],"by":[33,82],"under-determined":[35],"linear":[36],"measurements.":[37],"In":[38],"order":[39],"to":[40,72,133,136],"explore":[41],"spatial":[43],"redundancy":[44],"measurements,":[46],"corresponding":[48],"pixel":[49,78],"domain":[50],"information":[51,75],"extracted":[52],"using":[53],"structure":[55,84],"matrix.":[58,88],"Firstly,":[59],"mono-directional":[61,108],"prediction":[62,94,109],"modes":[63,95],"(i.e.":[64,96],"horizontal":[65],"mode":[66],"and":[67,124],"vertical":[68],"mode),":[69],"refer":[71],"nearest":[74],"neighboring":[77],"blocks,":[79],"obtained":[81,107],"Secondly,":[89],"we":[90],"design":[91],"Diagonal":[97,100],"+":[98,101],"Horizontal,":[99],"Vertical)":[102],"base":[103],"on":[104,129],"already":[106],"modes.":[110],"Experimental":[111],"results":[112],"show":[113],"that":[114],"this":[115],"improves":[117],"0.01":[118],"-":[119],"0.02":[120],"dB":[121],"PSNR":[122],"improvement":[123],"birate":[126],"reductions":[127],"average":[130],"19%,":[131],"up":[132],"36%":[134],"compared":[135],"state-of-the-art.":[138]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3116617544","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-01-04T11:51:58.774964","created_date":"2021-01-05"}