{"id":"https://openalex.org/W2970554730","doi":"https://doi.org/10.1109/icip.2019.8803253","title":"Partition Tree Guided Progressive Rethinking Network for in-Loop Filtering of HEVC","display_name":"Partition Tree Guided Progressive Rethinking Network for in-Loop Filtering of HEVC","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970554730","doi":"https://doi.org/10.1109/icip.2019.8803253","mag":"2970554730"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803253","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803253","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/A5008839625","display_name":"Dezhao Wang","orcid":"https://orcid.org/0009-0003-0138-0359"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dezhao Wang","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054386013","display_name":"Sifeng Xia","orcid":"https://orcid.org/0000-0003-0301-0004"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sifeng Xia","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070884682","display_name":"Wenhan Yang","orcid":"https://orcid.org/0000-0002-1692-0069"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhan Yang","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087570320","display_name":"Yueyu Hu","orcid":"https://orcid.org/0000-0003-4919-4515"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueyu Hu","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100761525","display_name":"Jiaying Liu","orcid":"https://orcid.org/0000-0002-0468-9576"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaying Liu","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5008839625"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.8196,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.91555174,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2671","last_page":"2675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10741","display_name":"Video Coding and Compression Technologies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9993000030517578,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.996999979019165,"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/partition","display_name":"Partition (number theory)","score":0.7110344767570496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6934834718704224},{"id":"https://openalex.org/keywords/loop","display_name":"Loop (graph theory)","score":0.5764469504356384},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4224322736263275},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.37941455841064453},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3528441786766052},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.33650267124176025},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19514372944831848},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.10367804765701294}],"concepts":[{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.7110344767570496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934834718704224},{"id":"https://openalex.org/C184670325","wikidata":"https://www.wikidata.org/wiki/Q512604","display_name":"Loop (graph theory)","level":2,"score":0.5764469504356384},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4224322736263275},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37941455841064453},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3528441786766052},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.33650267124176025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19514372944831848},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.10367804765701294}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803253","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803253","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":[{"score":0.6700000166893005,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W2017801928","https://openalex.org/W2035038275","https://openalex.org/W2101700394","https://openalex.org/W2123113293","https://openalex.org/W2142683286","https://openalex.org/W2146395539","https://openalex.org/W2194775991","https://openalex.org/W2242218935","https://openalex.org/W2508457857","https://openalex.org/W2510648513","https://openalex.org/W2566721764","https://openalex.org/W2741137940","https://openalex.org/W2757350145","https://openalex.org/W2791710889","https://openalex.org/W2799309438","https://openalex.org/W2942307329","https://openalex.org/W2963446712","https://openalex.org/W2964101377","https://openalex.org/W3102974666","https://openalex.org/W3104540617","https://openalex.org/W3104772632","https://openalex.org/W6675207249"],"related_works":["https://openalex.org/W24774503","https://openalex.org/W2014821076","https://openalex.org/W1990620319","https://openalex.org/W1965744717","https://openalex.org/W2111234595","https://openalex.org/W3216334292","https://openalex.org/W1883238101","https://openalex.org/W2481821631","https://openalex.org/W1536525683","https://openalex.org/W2482256034"],"abstract_inverted_index":{"In-Loop":[0],"filter":[1],"is":[2,45,84],"a":[3,31],"key":[4],"part":[5],"in":[6,100],"High":[7],"Efficiency":[8],"Video":[9],"Coding":[10,128],"(HEVC)":[11],"which":[12,83,137],"effectively":[13],"removes":[14],"the":[15,40,63,73,93,101,114,123,133,139,142,147,151,174],"compression":[16],"artifacts.":[17],"Recently,":[18],"many":[19],"newly":[20],"proposed":[21,161],"methods":[22],"combine":[23],"residual":[24],"learning":[25],"and":[26,47,76],"dense":[27],"connection":[28],"to":[29,68,79,86,98,131,173],"construct":[30],"deeper":[32],"network":[33,143,148],"for":[34],"better":[35],"in-loop":[36],"filtering":[37],"performance.":[38],"However,":[39],"long-term":[41,70],"dependency":[42],"between":[43,51,72],"blocks":[44,52,75],"neglected,":[46],"information":[48,78,135],"usually":[49,91],"passes":[50],"only":[53],"after":[54],"dimension":[55],"compression.":[56],"To":[57],"address":[58],"these":[59],"issues,":[60],"we":[61,121],"propose":[62],"Progressive":[64,115],"Rethinking":[65,116],"Block":[66],"(PRB)":[67],"deliver":[69],"memory":[71],"neighboring":[74],"allow":[77],"flow":[80],"without":[81],"compression,":[82],"similar":[85],"human":[87],"decision":[88],"mechanism":[89],"-":[90],"reviewing":[92],"complete":[94],"past":[95],"memorized":[96],"experiences":[97],"decide":[99],"present,":[102],"not":[103],"just":[104],"based":[105],"on":[106,170],"simple":[107],"principles":[108],"summarized":[109],"before.":[110],"PRBs":[111],"further":[112],"establish":[113],"Network":[117],"(PRN).":[118],"In":[119],"addition,":[120],"calculate":[122],"Multi-scale":[124],"Mean":[125],"value":[126],"of":[127,141,150],"Units":[129],"(MM-CU)":[130],"generate":[132],"side":[134],"maps":[136],"guide":[138],"training":[140],"by":[144],"novelly":[145],"telling":[146],"architecture":[149],"entire":[152],"coding":[153],"partition":[154,162],"tree.":[155],"Experimental":[156],"results":[157],"show":[158],"that":[159],"our":[160],"tree":[163],"guided":[164],"PRN":[165],"provides":[166],"10.1%":[167],"BD-rate":[168],"reduction":[169],"average":[171],"compared":[172],"HEVC":[175],"baseline.":[176]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
