{"id":"https://openalex.org/W4206734680","doi":"https://doi.org/10.1109/vcip53242.2021.9675326","title":"Optimization of Probability Distributions for Residual Coding of Screen Content","display_name":"Optimization of Probability Distributions for Residual Coding of Screen Content","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4206734680","doi":"https://doi.org/10.1109/vcip53242.2021.9675326"},"language":"en","primary_location":{"id":"doi:10.1109/vcip53242.2021.9675326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675326","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2212.01122","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010816597","display_name":"Hannah Och","orcid":"https://orcid.org/0009-0004-2526-6902"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Hannah Och","raw_affiliation_strings":["Friedrich-Alexander University Erlangen-N\u00fcrnberg (FAU) Multimedia Communications and Signal Processing, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander University Erlangen-N\u00fcrnberg (FAU) Multimedia Communications and Signal Processing, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002596904","display_name":"Tilo Strutz","orcid":"https://orcid.org/0000-0001-5063-6515"},"institutions":[{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]},{"id":"https://openalex.org/I4210158023","display_name":"Hochschule f\u00fcr Telekommunikation Leipzig","ror":"https://ror.org/05k9vmz93","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210158023"]},{"id":"https://openalex.org/I4210093367","display_name":"Deutsche Telekom (Germany)","ror":"https://ror.org/00m8prc86","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210093367"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tilo Strutz","raw_affiliation_strings":["Deutsche Telekom AG, Leipzig University of Telecommunications Institute of Communications Engineering, Leipzig, Germany"],"affiliations":[{"raw_affiliation_string":"Deutsche Telekom AG, Leipzig University of Telecommunications Institute of Communications Engineering, Leipzig, Germany","institution_ids":["https://openalex.org/I4210158023","https://openalex.org/I4210093367","https://openalex.org/I926574661"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062850220","display_name":"Andr\u00e9 Kaup","orcid":"https://orcid.org/0000-0002-0929-5074"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andre Kaup","raw_affiliation_strings":["Friedrich-Alexander University Erlangen-N\u00fcrnberg (FAU) Multimedia Communications and Signal Processing, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander University Erlangen-N\u00fcrnberg (FAU) Multimedia Communications and Signal Processing, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010816597"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":null,"apc_paid":null,"fwci":0.2628,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.66017471,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9998999834060669,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9998999834060669,"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/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/T11165","display_name":"Image and Video Quality Assessment","score":0.9958999752998352,"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":0.6962352991104126},{"id":"https://openalex.org/keywords/context-adaptive-binary-arithmetic-coding","display_name":"Context-adaptive binary arithmetic coding","score":0.6452783942222595},{"id":"https://openalex.org/keywords/arithmetic-coding","display_name":"Arithmetic coding","score":0.5883730053901672},{"id":"https://openalex.org/keywords/context-adaptive-variable-length-coding","display_name":"Context-adaptive variable-length coding","score":0.5716425776481628},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.5523276925086975},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5429853796958923},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5391983985900879},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49636727571487427},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4786742329597473},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.47064918279647827},{"id":"https://openalex.org/keywords/color-depth","display_name":"Color depth","score":0.4401625692844391},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.429239422082901},{"id":"https://openalex.org/keywords/tunstall-coding","display_name":"Tunstall coding","score":0.4267972409725189},{"id":"https://openalex.org/keywords/variable-length-code","display_name":"Variable-length code","score":0.41786640882492065},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40446701645851135},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.34284478425979614},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.29226893186569214},{"id":"https://openalex.org/keywords/color-image","display_name":"Color image","score":0.20240098237991333},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.185922771692276},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.12680649757385254},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07421866059303284}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6962352991104126},{"id":"https://openalex.org/C175732694","wikidata":"https://www.wikidata.org/wiki/Q1128713","display_name":"Context-adaptive binary arithmetic coding","level":3,"score":0.6452783942222595},{"id":"https://openalex.org/C153338461","wikidata":"https://www.wikidata.org/wiki/Q2651","display_name":"Arithmetic coding","level":4,"score":0.5883730053901672},{"id":"https://openalex.org/C135534801","wikidata":"https://www.wikidata.org/wiki/Q1128721","display_name":"Context-adaptive variable-length coding","level":4,"score":0.5716425776481628},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5523276925086975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5429853796958923},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5391983985900879},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49636727571487427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4786742329597473},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.47064918279647827},{"id":"https://openalex.org/C91522604","wikidata":"https://www.wikidata.org/wiki/Q690110","display_name":"Color depth","level":5,"score":0.4401625692844391},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.429239422082901},{"id":"https://openalex.org/C73231260","wikidata":"https://www.wikidata.org/wiki/Q7853376","display_name":"Tunstall coding","level":4,"score":0.4267972409725189},{"id":"https://openalex.org/C60603091","wikidata":"https://www.wikidata.org/wiki/Q2981616","display_name":"Variable-length code","level":3,"score":0.41786640882492065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40446701645851135},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.34284478425979614},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.29226893186569214},{"id":"https://openalex.org/C142616399","wikidata":"https://www.wikidata.org/wiki/Q5148604","display_name":"Color image","level":4,"score":0.20240098237991333},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.185922771692276},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.12680649757385254},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07421866059303284}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/vcip53242.2021.9675326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675326","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2212.01122","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.01122","pdf_url":"https://arxiv.org/pdf/2212.01122","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2212.01122","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.01122","pdf_url":"https://arxiv.org/pdf/2212.01122","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G744934946","display_name":null,"funder_award_id":"438221930","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1496489693","https://openalex.org/W1561538450","https://openalex.org/W2008261974","https://openalex.org/W2064378236","https://openalex.org/W2099563019","https://openalex.org/W2130350995","https://openalex.org/W2146395539","https://openalex.org/W2152066778","https://openalex.org/W2249152912","https://openalex.org/W2295929469","https://openalex.org/W2509267076","https://openalex.org/W2512858349","https://openalex.org/W2518435514","https://openalex.org/W2522566846","https://openalex.org/W2526062435","https://openalex.org/W2556261813","https://openalex.org/W2658889823","https://openalex.org/W2782579108","https://openalex.org/W2973017283","https://openalex.org/W2991645662","https://openalex.org/W3015790084","https://openalex.org/W3042112328","https://openalex.org/W3154281209","https://openalex.org/W6682510536"],"related_works":["https://openalex.org/W2148686647","https://openalex.org/W4312628177","https://openalex.org/W1579872668","https://openalex.org/W2123072960","https://openalex.org/W2045024766","https://openalex.org/W2213686625","https://openalex.org/W1994532706","https://openalex.org/W2984083444","https://openalex.org/W2131702615","https://openalex.org/W2375774096"],"abstract_inverted_index":{"Probability":[0],"distribution":[1,35],"modeling":[2,147],"is":[3,20,36,84,90,100,131,163],"the":[4,40,44,50,58,69,86,88,127,136,140,155,161,187],"basis":[5],"for":[6,10,139,148],"most":[7],"competitive":[8],"methods":[9],"lossless":[11],"coding":[12,108,111,118,129],"of":[13,46,157,172],"screen":[14],"content.":[15],"One":[16],"such":[17],"state-of-the-art":[18],"method":[19,190],"known":[21],"as":[22],"soft":[23,121],"context":[24,122],"formation":[25],"(SCF).":[26],"For":[27],"each":[28],"pixel":[29,59],"to":[30,174,180],"be":[31,63],"encoded,":[32],"a":[33,79,94,150,169],"probability":[34,137,146],"estimated":[37],"based":[38,119],"on":[39,120,154,177,192],"neighboring":[41],"pattern":[42,48],"and":[43,109,185,197],"occurrence":[45,156],"that":[47,68],"in":[49,76,160,168],"already":[51],"encoded":[52,64,92],"image.":[53],"Using":[54],"an":[55,144],"arithmetic":[56],"coder,":[57],"color":[60,71,89,95,152],"can":[61],"thus":[62],"very":[65],"efficiently,":[66],"provided":[67],"current":[70],"has":[72],"been":[73],"observed":[74],"before":[75],"association":[77],"with":[78],"similar":[80],"pattern.":[81],"If":[82],"this":[83,125],"not":[85],"case,":[87],"instead":[91],"using":[93],"palette":[96],"or,":[97],"if":[98],"it":[99],"still":[101],"unknown,":[102],"via":[103],"residual":[104,110,128,141],"coding.":[105],"Both":[106],"palette-based":[107],"have":[112],"significantly":[113],"worse":[114],"compression":[115],"efficiency":[116],"than":[117],"formation.":[123],"In":[124],"paper,":[126],"stage":[130],"improved":[132,188],"by":[133],"adaptively":[134],"trimming":[135],"distributions":[138],"error.":[142],"Furthermore,":[143],"enhanced":[145],"indicating":[149],"new":[151,158],"depending":[153],"colors":[159],"neighborhood":[162],"proposed.":[164],"These":[165],"modifications":[166],"result":[167],"bitrate":[170],"reduction":[171],"up":[173],"2.9":[175],"%":[176,196,199],"average.":[178],"Compared":[179],"HEVC":[181],"(HM-16.21":[182],"+":[183],"SCM-8.8)":[184],"FLIF,":[186],"SCF":[189],"saves":[191],"average":[193],"about":[194],"11":[195],"18":[198],"rate,":[200],"respectively.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}