{"id":"https://openalex.org/W2970152706","doi":"https://doi.org/10.1109/icip.2019.8803456","title":"Blocksize-QP Dependent Intra Interpolation Filters","display_name":"Blocksize-QP Dependent Intra Interpolation Filters","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970152706","doi":"https://doi.org/10.1109/icip.2019.8803456","mag":"2970152706"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803456","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5028712297","display_name":"Yoshitaka Kidani","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshitaka Kidani","raw_affiliation_strings":["KDDI Research, Inc, Fujimino, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc, Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102301632","display_name":"Kei Kawamura","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kei Kawamura","raw_affiliation_strings":["KDDI Research, Inc, Fujimino, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc, Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079470198","display_name":"Kyohei Unno","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kyohei Unno","raw_affiliation_strings":["KDDI Research, Inc, Fujimino, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc, Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109349308","display_name":"Sei Naito","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sei Naito","raw_affiliation_strings":["KDDI Research, Inc, Fujimino, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc, Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210164495"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.08332826,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4125","last_page":"4129"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10741","display_name":"Video Coding and Compression Technologies","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10741","display_name":"Video Coding and Compression Technologies","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9983999729156494,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9980000257492065,"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/interpolation","display_name":"Interpolation (computer graphics)","score":0.6728724241256714},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5943588018417358},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5619248747825623},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.49625450372695923},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.493068665266037},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4725354015827179},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43248844146728516},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27353233098983765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2527714967727661},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06573361158370972},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.062084347009658813}],"concepts":[{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6728724241256714},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5943588018417358},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5619248747825623},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.49625450372695923},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.493068665266037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4725354015827179},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43248844146728516},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27353233098983765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2527714967727661},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06573361158370972},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.062084347009658813},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803456","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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":7,"referenced_works":["https://openalex.org/W1480014140","https://openalex.org/W2101700394","https://openalex.org/W2537640770","https://openalex.org/W2610467092","https://openalex.org/W2979673094","https://openalex.org/W6628911657","https://openalex.org/W6728742733"],"related_works":["https://openalex.org/W1974589459","https://openalex.org/W2161295375","https://openalex.org/W1554212698","https://openalex.org/W2383239512","https://openalex.org/W2793693916","https://openalex.org/W2016837818","https://openalex.org/W2403974694","https://openalex.org/W1976332093","https://openalex.org/W2742242928","https://openalex.org/W2367368021"],"abstract_inverted_index":{"Intra":[0],"interpolation":[1,37,44],"filters":[2,38,45,52],"for":[3,105],"intra":[4,17,107],"angular":[5,18],"prediction":[6,19],"play":[7],"an":[8],"important":[9],"role":[10],"in":[11,115],"the":[12,16,27,50,64,76,85,100,110],"coding":[13,30,86],"performance.":[14,87],"In":[15,68],"of":[20],"VVC,":[21],"which":[22],"is":[23,46],"being":[24,47],"standardized":[25],"by":[26,99],"joint":[28],"video":[29],"expert":[31],"team":[32],"(JVET),":[33],"block-size":[34,77],"based":[35,73],"switchable":[36],"between":[39],"4-tap":[40],"cubic":[41],"and":[42,78],"Gaussian":[43],"studied.":[48],"Although":[49],"two":[51],"have":[53],"different":[54],"frequency":[55],"characteristics,":[56],"block":[57],"size-based":[58],"criteria":[59,72],"are":[60,81],"insufficient":[61],"to":[62,83],"represent":[63],"reference":[65],"sample":[66],"characteristics.":[67],"this":[69],"manuscript,":[70],"switching":[71],"on":[74],"both":[75],"QP":[79],"value":[80],"proposed":[82],"improve":[84],"The":[88],"experimental":[89],"results":[90],"show":[91],"a":[92],"-0.45%":[93],"BD-rate":[94],"gain":[95],"compared":[96],"with":[97],"that":[98],"VVC":[101],"test":[102,112],"model":[103],"2":[104],"all":[106],"conditions":[108],"under":[109],"common":[111],"condition":[113],"(CTC)":[114],"JVET.":[116]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
