{"id":"https://openalex.org/W2527408608","doi":"https://doi.org/10.1109/dmiaf.2016.7574904","title":"HDR videocompression with VPX","display_name":"HDR videocompression with VPX","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2527408608","doi":"https://doi.org/10.1109/dmiaf.2016.7574904","mag":"2527408608"},"language":"en","primary_location":{"id":"doi:10.1109/dmiaf.2016.7574904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dmiaf.2016.7574904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Digital Media Industry &amp; Academic Forum (DMIAF)","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/A5061338667","display_name":"Pankaj Topiwala","orcid":null},"institutions":[{"id":"https://openalex.org/I4210096332","display_name":"Fastvdo (United States)","ror":"https://ror.org/00nq2vw96","country_code":"US","type":"company","lineage":["https://openalex.org/I4210096332"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pankaj Topiwala","raw_affiliation_strings":["FastVDO LLC, 3097 Cortona Drive, Melbourne, FL, USA","FastVDO LLC, Melbourne, FL, USA"],"affiliations":[{"raw_affiliation_string":"FastVDO LLC, 3097 Cortona Drive, Melbourne, FL, USA","institution_ids":["https://openalex.org/I4210096332"]},{"raw_affiliation_string":"FastVDO LLC, Melbourne, FL, USA","institution_ids":["https://openalex.org/I4210096332"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100671164","display_name":"Wei Dai","orcid":"https://orcid.org/0000-0002-7571-4863"},"institutions":[{"id":"https://openalex.org/I4210096332","display_name":"Fastvdo (United States)","ror":"https://ror.org/00nq2vw96","country_code":"US","type":"company","lineage":["https://openalex.org/I4210096332"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Dai","raw_affiliation_strings":["FastVDO LLC, 3097 Cortona Drive, Melbourne, FL, USA","FastVDO LLC, Melbourne, FL, USA"],"affiliations":[{"raw_affiliation_string":"FastVDO LLC, 3097 Cortona Drive, Melbourne, FL, USA","institution_ids":["https://openalex.org/I4210096332"]},{"raw_affiliation_string":"FastVDO LLC, Melbourne, FL, USA","institution_ids":["https://openalex.org/I4210096332"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082511351","display_name":"Madhu Krishnan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210096332","display_name":"Fastvdo (United States)","ror":"https://ror.org/00nq2vw96","country_code":"US","type":"company","lineage":["https://openalex.org/I4210096332"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Madhu Krishnan","raw_affiliation_strings":["FastVDO LLC, 3097 Cortona Drive, Melbourne, FL, USA","FastVDO LLC, Melbourne, FL, USA"],"affiliations":[{"raw_affiliation_string":"FastVDO LLC, 3097 Cortona Drive, Melbourne, FL, USA","institution_ids":["https://openalex.org/I4210096332"]},{"raw_affiliation_string":"FastVDO LLC, Melbourne, FL, USA","institution_ids":["https://openalex.org/I4210096332"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061338667"],"corresponding_institution_ids":["https://openalex.org/I4210096332"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09851142,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"65","last_page":"70"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement 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/T11019","display_name":"Image Enhancement 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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9943000078201294,"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/computer-science","display_name":"Computer science","score":0.787121057510376},{"id":"https://openalex.org/keywords/codec","display_name":"Codec","score":0.7602460384368896},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6598047018051147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5698949694633484},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.549430251121521},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4672916531562805},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.15120553970336914},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09208297729492188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.787121057510376},{"id":"https://openalex.org/C161765866","wikidata":"https://www.wikidata.org/wiki/Q184748","display_name":"Codec","level":2,"score":0.7602460384368896},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6598047018051147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5698949694633484},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.549430251121521},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4672916531562805},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.15120553970336914},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09208297729492188},{"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/dmiaf.2016.7574904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dmiaf.2016.7574904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Digital Media Industry &amp; Academic Forum (DMIAF)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2101700394","https://openalex.org/W2135948127"],"related_works":["https://openalex.org/W2755342338","https://openalex.org/W2058170566","https://openalex.org/W2036807459","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2166024367","https://openalex.org/W2772917594","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2079911747"],"abstract_inverted_index":{"This":[0],"paper":[1],"present":[2],"approaches":[3],"to":[4,57,162],"code":[5],"HDR":[6,34,180],"video":[7,13,35,89],"using":[8],"VP10,":[9],"an":[10],"open":[11],"source":[12],"codec":[14],"which":[15,94,158],"is":[16,106,154,160],"part":[17],"of":[18,53,68,176],"the":[19,58,97,103],"WebM":[20],"project":[21],"developed":[22,28,39],"by":[23],"Google.":[24],"Three":[25],"techniques":[26],"are":[27,171],"in":[29,41,102,179],"this":[30],"paper:":[31],"1)":[32],"An":[33],"coding":[36,181],"processing":[37],"chain":[38],"recently":[40],"standards":[42],"committees":[43],"(the":[44],"Joint":[45],"Collaborative":[46],"Team":[47],"on":[48,190],"Video":[49],"Coding,":[50],"or":[51],"JCT-VC,":[52],"ISO/IEC/ITU),":[54],"but":[55],"adapted":[56],"VP10":[59],"codec,":[60],"herein":[61,79],"called":[62,80],"HDR_VP10.":[63,191],"2)":[64],"A":[65],"modified":[66],"version":[67],"HDR_VP10":[69,105],"with":[70],"different":[71],"intermediate":[72],"color":[73],"transforms":[74],"and":[75,82,113,150],"advanced":[76],"sampling":[77],"filters":[78],"FastVDO_ECHDR_VP10":[81],"3)":[83],"FastVDO_HDR_VP10":[84,183],"that":[85,167],"uses":[86],"a":[87,185],"new":[88],"data":[90],"adaptive":[91],"tuning":[92],"process,":[93],"differs":[95],"from":[96],"ST.2084":[98],"transfer":[99],"function":[100],"used":[101,107],"anchor.":[104],"as":[108],"reference":[109],"point":[110],"for":[111,119,127,141,157],"subjective":[112],"objective":[114,117,169],"comparisons.":[115],"Representative":[116],"results":[118,126,140],"these":[120],"systems":[121],"include:":[122],"(a)":[123],"FastVDO_ECHDR_VP10,":[124],"Overall":[125,139],"RGB-PSNR,":[128,142],"DE100,":[129,143],"MD100,":[130,144],"PSNRL100":[131,145],"were":[132,146],"8.0%,":[133],"-3.5%,":[134],"46.2%,":[135],"2.9%;":[136],"(b)":[137],"FastVDO_HDR_VP10,":[138],"-40.2%,":[147],"-17.0%,":[148],"-5.5%":[149],"-2.8%":[151],"respectively.":[152],"It":[153],"asserted":[155],"(and":[156],"there":[159],"appears":[161],"be":[163],"fairly":[164],"broad":[165],"agreement)":[166],"such":[168],"metrics":[170],"currently":[172],"not":[173],"very":[174],"predictive":[175],"visual":[177,187],"quality":[178,188],"studies.":[182],"shows":[184],"significant":[186],"improvement":[189]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
