{"id":"https://openalex.org/W3090077228","doi":"https://doi.org/10.1109/icip40778.2020.9190906","title":"Machine Learning Based Symbol Probability Distribution Prediction For Entropy Coding In Av1","display_name":"Machine Learning Based Symbol Probability Distribution Prediction For Entropy Coding In Av1","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3090077228","doi":"https://doi.org/10.1109/icip40778.2020.9190906","mag":"3090077228"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9190906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190906","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5100438163","display_name":"Mingliang Chen","orcid":"https://orcid.org/0000-0003-2005-4295"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mingliang Chen","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064445567","display_name":"Hui Su","orcid":"https://orcid.org/0000-0003-0340-1128"},"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":"Hui Su","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080104324","display_name":"Sai Deng","orcid":"https://orcid.org/0000-0002-3681-4888"},"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":"Sai Deng","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103766279","display_name":"Yaowu Xu","orcid":null},"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":"Yaowu Xu","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100438163"],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.1114549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3374","last_page":"3378"},"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9991999864578247,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9890000224113464,"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/entropy-encoding","display_name":"Entropy encoding","score":0.7384326457977295},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6780024766921997},{"id":"https://openalex.org/keywords/arithmetic-coding","display_name":"Arithmetic coding","score":0.6606044173240662},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.626335084438324},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5672875642776489},{"id":"https://openalex.org/keywords/lookup-table","display_name":"Lookup table","score":0.5386412739753723},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5117949843406677},{"id":"https://openalex.org/keywords/tunstall-coding","display_name":"Tunstall coding","score":0.5097827315330505},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5044742822647095},{"id":"https://openalex.org/keywords/huffman-coding","display_name":"Huffman coding","score":0.487932413816452},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.45634210109710693},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.43719756603240967},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3746081590652466},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34082019329071045},{"id":"https://openalex.org/keywords/context-adaptive-binary-arithmetic-coding","display_name":"Context-adaptive binary arithmetic coding","score":0.3390527069568634},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28381162881851196},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20126089453697205}],"concepts":[{"id":"https://openalex.org/C1769480","wikidata":"https://www.wikidata.org/wiki/Q1345239","display_name":"Entropy encoding","level":3,"score":0.7384326457977295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6780024766921997},{"id":"https://openalex.org/C153338461","wikidata":"https://www.wikidata.org/wiki/Q2651","display_name":"Arithmetic coding","level":4,"score":0.6606044173240662},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.626335084438324},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5672875642776489},{"id":"https://openalex.org/C134835016","wikidata":"https://www.wikidata.org/wiki/Q690265","display_name":"Lookup table","level":2,"score":0.5386412739753723},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5117949843406677},{"id":"https://openalex.org/C73231260","wikidata":"https://www.wikidata.org/wiki/Q7853376","display_name":"Tunstall coding","level":4,"score":0.5097827315330505},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5044742822647095},{"id":"https://openalex.org/C46900642","wikidata":"https://www.wikidata.org/wiki/Q2647","display_name":"Huffman coding","level":3,"score":0.487932413816452},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.45634210109710693},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.43719756603240967},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3746081590652466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34082019329071045},{"id":"https://openalex.org/C175732694","wikidata":"https://www.wikidata.org/wiki/Q1128713","display_name":"Context-adaptive binary arithmetic coding","level":3,"score":0.3390527069568634},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28381162881851196},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20126089453697205},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9190906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190906","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2034196558","https://openalex.org/W2089566934","https://openalex.org/W2146395539","https://openalex.org/W2754162958","https://openalex.org/W2765360690","https://openalex.org/W2891639355","https://openalex.org/W2891963226","https://openalex.org/W3105615783","https://openalex.org/W6658953820"],"related_works":["https://openalex.org/W2156106714","https://openalex.org/W1499106570","https://openalex.org/W1579872668","https://openalex.org/W4312628177","https://openalex.org/W2096793045","https://openalex.org/W2123072960","https://openalex.org/W340392727","https://openalex.org/W1994532706","https://openalex.org/W4297360799","https://openalex.org/W2064300363"],"abstract_inverted_index":{"Entropy":[0],"coding":[1,23,39,156,172],"is":[2,9,40,45,149],"a":[3,131],"lossless":[4],"data":[5],"compression":[6],"technique":[7],"that":[8,54,82,136,164],"widely":[10],"applied":[11],"in":[12,113,151],"video":[13],"codecs":[14],"to":[15,60,78,87],"encode":[16],"syntax":[17],"elements":[18],"into":[19,124],"bitstreams.":[20],"Efficient":[21],"entropy":[22,62,74,144,155,171],"requires":[24],"accurate":[25,139],"prediction":[26,142,159],"of":[27,31,96,102,157,170],"the":[28,32,56,89,94,97,100,109,114,154,168],"probability":[29,44,110,141],"distribution":[30],"encoded":[33],"symbols.":[34],"In":[35,126],"AV1,":[36],"multi-symbol":[37],"arithmetic":[38],"adopted.":[41],"The":[42,64,73,146],"symbol":[43,140],"derived":[46],"with":[47],"handcrafted":[48],"context":[49,75],"models":[50],"and":[51],"lookup":[52,65,90,98,115],"tables":[53,116],"store":[55],"predicted":[57],"probabilities":[58],"corresponding":[59],"different":[61],"contexts.":[63],"table":[66],"based":[67,134],"scheme":[68,135],"has":[69],"some":[70],"fundamental":[71],"deficiencies.":[72],"features":[76],"have":[77],"be":[79,85,105],"discrete":[80],"so":[81],"they":[83],"can":[84,166],"used":[86],"index":[88],"tables.":[91],"To":[92],"reduce":[93],"size":[95],"table,":[99],"number":[101],"contexts":[103],"cannot":[104],"very":[106],"large.":[107],"Moreover,":[108],"distributions":[111],"stored":[112],"are":[117],"maintained":[118],"separately":[119],"without":[120],"taking":[121],"their":[122],"correlations":[123],"consideration.":[125],"this":[127],"paper,":[128],"we":[129],"propose":[130],"machine":[132],"learning":[133],"achieves":[137],"more":[138],"for":[143,153],"coding.":[145],"proposed":[147],"approach":[148],"implemented":[150],"AV1":[152],"intra":[158],"modes.":[160],"Experimental":[161],"results":[162],"demonstrate":[163],"it":[165],"improve":[167],"efficiency":[169],"significantly.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}