{"id":"https://openalex.org/W3015368828","doi":"https://doi.org/10.1109/icassp40776.2020.9054166","title":"Semi-Regular Geometric Kernel Encoding &amp; Reconstruction for Video Compression","display_name":"Semi-Regular Geometric Kernel Encoding &amp; Reconstruction for Video Compression","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015368828","doi":"https://doi.org/10.1109/icassp40776.2020.9054166","mag":"3015368828"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9054166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5030696708","display_name":"Xiaochong Jiang","orcid":"https://orcid.org/0009-0003-8343-6394"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaochong Jiang","raw_affiliation_strings":["Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075921874","display_name":"Cheng Yang","orcid":"https://orcid.org/0000-0002-3540-1598"},"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":"Cheng Yang","raw_affiliation_strings":["York University, Toronto, Canada"],"affiliations":[{"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"],"affiliations":[{"raw_affiliation_string":"York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111184745","display_name":"Seishi Takamura","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Seishi Takamura","raw_affiliation_strings":["NTT Corporation, Yokosuka, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Yokosuka, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030696708"],"corresponding_institution_ids":["https://openalex.org/I38877650"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02865161,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"2183","last_page":"2187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9990000128746033,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9990000128746033,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987000226974487,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9986000061035156,"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":0.678002655506134},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6296187043190002},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.6253911852836609},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.5962616205215454},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5795416831970215},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4726601839065552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4356878995895386},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3789494037628174},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19622355699539185},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.10226655006408691}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.678002655506134},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6296187043190002},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.6253911852836609},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5962616205215454},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5795416831970215},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4726601839065552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4356878995895386},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3789494037628174},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19622355699539185},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.10226655006408691},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9054166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W604015344","https://openalex.org/W1987296295","https://openalex.org/W2003106976","https://openalex.org/W2011658373","https://openalex.org/W2021168822","https://openalex.org/W2033819227","https://openalex.org/W2056341019","https://openalex.org/W2085261163","https://openalex.org/W2101777189","https://openalex.org/W2109424729","https://openalex.org/W2112698054","https://openalex.org/W2135887870","https://openalex.org/W2152327564","https://openalex.org/W2166800185","https://openalex.org/W2427389564","https://openalex.org/W2462174551","https://openalex.org/W2471962767","https://openalex.org/W2785875810","https://openalex.org/W2890343135","https://openalex.org/W2913535645","https://openalex.org/W2937614694","https://openalex.org/W3001632353","https://openalex.org/W4250589301","https://openalex.org/W6758920291"],"related_works":["https://openalex.org/W4235381733","https://openalex.org/W2355022049","https://openalex.org/W2060429446","https://openalex.org/W2741782512","https://openalex.org/W3011302839","https://openalex.org/W2392958391","https://openalex.org/W3155227409","https://openalex.org/W2898682874","https://openalex.org/W2612632602","https://openalex.org/W2321805087"],"abstract_inverted_index":{"Conventional":[0],"video":[1,22],"coding":[2,12,195],"schemes":[3],"employ":[4],"a":[5,34,37,43,75,81,86,94,142,185],"hybrid":[6],"motion":[7],"prediction":[8,176],"/":[9],"residual":[10],"transform":[11],"paradigm,":[13],"which":[14,89,133],"only":[15],"exploits":[16],"redundancy":[17,58],"in":[18,31,36,42,67,85,129,192],"individual":[19],"pairs":[20],"of":[21,126],"frames":[23],"for":[24,98],"compression":[25],"gain.":[26],"However,":[27],"rigid":[28],"geometric":[29,55,78,187],"structures":[30],"3D":[32,124],"space\u2014e.g.,":[33],"building":[35],"scene\u2019s":[38],"background\u2014persist":[39],"across":[40,59],"time":[41],"large":[44],"frame":[45,62,87,139,169],"group.":[46],"Thus":[47],"if":[48],"one":[49,68],"can":[50,64,114],"extract":[51,74],"and":[52,173,201],"encode":[53,152],"the":[54,60,123,130,138,149,153,171,174],"structure,":[56],"then":[57,165],"entire":[61],"group":[63,140],"be":[65,115],"removed":[66],"shot.":[69],"In":[70],"this":[71],"paper,":[72],"we":[73,103,134],"best-fitting":[76],"\"semi-regular\"":[77],"structure":[79,120],"from":[80,137],"target":[82],"spatial":[83],"region":[84],"group,":[88],"is":[90,107],"encoded":[91,116],"separately":[92],"as":[93,156,170],"unified":[95],"signal":[96],"predictor":[97],"these":[99],"frames.":[100],"By":[101],"semi-regular,":[102],"mean":[104],"its":[105,111],"geometry":[106],"simple":[108],"enough":[109],"that":[110,183],"shape":[112,125],"parameters":[113],"cheaply.":[117],"This":[118],"semi-regular":[119,186],"kernel":[121,205],"approximates":[122],"an":[127,157],"object":[128],"video,":[131],"on":[132,148],"project":[135],"pixels":[136,155,163],"to":[141,167],"carefully":[143],"spaced":[144],"2D":[145,190],"grid":[146],"overlaid":[147],"kernel.":[150],"We":[151],"projected":[154],"intra-frame":[158],"using":[159],"HEVC.":[160],"The":[161],"decoded":[162],"are":[164,178],"back-projected":[166],"each":[168],"predictor,":[172],"resulting":[175],"residuals":[177],"transform-coded.":[179],"Experimental":[180],"results":[181],"show":[182],"employing":[184],"kernel\u2014a":[188],"folded":[189],"plane":[191],"our":[193,202],"realization\u2014improves":[194],"performance":[196],"over":[197],"native":[198],"HEVC":[199],"implementation":[200],"previous":[203],"regular":[204],"based":[206],"scheme.":[207]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
