{"id":"https://openalex.org/W2970450539","doi":"https://doi.org/10.1109/icip.2019.8803127","title":"AV1 in-loop Filtering using a Wide-Activation Structured Residual Network","display_name":"AV1 in-loop Filtering using a Wide-Activation Structured Residual Network","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970450539","doi":"https://doi.org/10.1109/icip.2019.8803127","mag":"2970450539"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803127","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/A5101970918","display_name":"Guangyao Chen","orcid":"https://orcid.org/0000-0002-6351-9083"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyao Chen","raw_affiliation_strings":["Hangzhou Normal University","Hangzhou Normal University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Normal University","institution_ids":["https://openalex.org/I163151501"]},{"raw_affiliation_string":"Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028059517","display_name":"Dandan Ding","orcid":"https://orcid.org/0000-0003-2911-1321"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dandan Ding","raw_affiliation_strings":["Hangzhou Normal University","Hangzhou Normal University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Normal University","institution_ids":["https://openalex.org/I163151501"]},{"raw_affiliation_string":"Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024859042","display_name":"Debargha Mukherjee","orcid":"https://orcid.org/0000-0002-9380-7377"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Debargha Mukherjee","raw_affiliation_strings":["Google Inc","Google Inc., Menlo Park, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036896344","display_name":"Urvang Joshi","orcid":"https://orcid.org/0000-0001-9590-9505"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Urvang Joshi","raw_affiliation_strings":["Google Inc","Google Inc., Menlo Park, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049456852","display_name":"Yue Chen","orcid":"https://orcid.org/0000-0001-7504-8517"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Chen","raw_affiliation_strings":["Google Inc","Google Inc., Menlo Park, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Inc., Menlo Park, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8933,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.78934877,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1725","last_page":"1729"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9995999932289124,"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/T11019","display_name":"Image Enhancement 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"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.8246749639511108},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8034477233886719},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.7121256589889526},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6128526329994202},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5307143926620483},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4577142596244812},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.44591382145881653},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3908100128173828},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08587926626205444}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8246749639511108},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8034477233886719},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.7121256589889526},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6128526329994202},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5307143926620483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4577142596244812},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.44591382145881653},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3908100128173828},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08587926626205444},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803127","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":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2142683286","https://openalex.org/W2194775991","https://openalex.org/W2332316162","https://openalex.org/W2477177239","https://openalex.org/W2508457857","https://openalex.org/W2510648513","https://openalex.org/W2612222456","https://openalex.org/W2757350145","https://openalex.org/W2791542696","https://openalex.org/W2792186562","https://openalex.org/W2792275277","https://openalex.org/W2792766850","https://openalex.org/W2793408333","https://openalex.org/W2799309438","https://openalex.org/W2884796644","https://openalex.org/W2891639355","https://openalex.org/W2892257692","https://openalex.org/W3100087914","https://openalex.org/W3102974666","https://openalex.org/W3104540617","https://openalex.org/W3104772632","https://openalex.org/W4239851455","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W3181746755","https://openalex.org/W3016958897","https://openalex.org/W2782645198","https://openalex.org/W3052481912","https://openalex.org/W2275988210","https://openalex.org/W2961623865","https://openalex.org/W2374658657","https://openalex.org/W3120495829","https://openalex.org/W2170630590","https://openalex.org/W2015942816"],"abstract_inverted_index":{"The":[0],"in-loop":[1,35,45],"filter,":[2,46],"which":[3],"constitutes":[4],"an":[5,103],"important":[6],"part":[7],"in":[8,33,120,136],"modern":[9],"video":[10,84],"coding,":[11,89,140],"improves":[12],"both":[13],"subjective":[14],"and":[15,132,138,145],"objective":[16],"quality":[17],"of":[18,58,77,115,147],"reconstructed":[19],"frames.":[20],"Lately,":[21],"Convolutional":[22],"Neural":[23],"Network":[24,51,62],"(CNN)":[25],"has":[26],"demonstrated":[27],"its":[28],"superiority":[29],"over":[30],"traditional":[31],"methods":[32],"addressing":[34],"filtering":[36],"problem.":[37],"In":[38],"this":[39,101],"paper,":[40],"we":[41,64,107],"develop":[42],"a":[43,73,109],"CNN-based":[44],"namely":[47],"Wide":[48],"Activation":[49],"Residual":[50,61],"(WARN),":[52],"for":[53],"AV1":[54],"encoder.":[55],"On":[56],"top":[57],"the":[59,95,116,143],"plain":[60],"(ResNet),":[63],"introduce":[65],"wide":[66],"activation":[67],"to":[68,87,93,130],"each":[69],"residual":[70],"block,":[71],"making":[72],"more":[74],"reasonable":[75],"allocation":[76],"network":[78],"parameters.":[79],"When":[80],"incorporating":[81],"WARN":[82,127],"into":[83],"encoder,":[85],"particular":[86],"inter":[88,139],"it":[90],"is":[91],"intricate":[92],"obtain":[94],"global":[96],"optimum":[97],"performance.":[98],"After":[99],"simplifying":[100],"as":[102],"end-to-end":[104],"trainable":[105],"problem,":[106],"propose":[108],"skipping":[110],"method":[111],"by":[112],"taking":[113],"advantage":[114],"hierarchical":[117],"reference":[118],"structure":[119],"AV1.":[121],"Experimental":[122],"results":[123],"show":[124],"that":[125],"our":[126,148],"achieves":[128],"up":[129],"14.42%":[131],"9.64%":[133],"BD-rate":[134],"reduction":[135],"intra":[137],"respectively.":[141],"All":[142],"code":[144],"model":[146],"approach":[149],"are":[150],"available":[151],"at":[152],"https://github.com/IVC-Projects/AV1_WARN.":[153]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-02-13T13:36:01.753593","created_date":"2025-10-10T00:00:00"}
