{"id":"https://openalex.org/W3045851159","doi":"https://doi.org/10.1109/iwcmc48107.2020.9148248","title":"CNN based HDR/WCG Characteristic Detection for Ultra-High Definition Video","display_name":"CNN based HDR/WCG Characteristic Detection for Ultra-High Definition Video","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3045851159","doi":"https://doi.org/10.1109/iwcmc48107.2020.9148248","mag":"3045851159"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc48107.2020.9148248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc48107.2020.9148248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Wireless Communications and Mobile Computing (IWCMC)","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/A5101907040","display_name":"Yun Zhou","orcid":"https://orcid.org/0000-0002-2306-8986"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yun Zhou","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050750924","display_name":"Xiao Hu","orcid":"https://orcid.org/0000-0003-1128-4099"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Hu","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102864522","display_name":"Xiaoqiang Guo","orcid":"https://orcid.org/0000-0003-2355-0569"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqiang Guo","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100449177","display_name":"Xiaoyu Li","orcid":"https://orcid.org/0000-0003-2588-1687"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Li","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418684","display_name":"Xiaoli Li","orcid":"https://orcid.org/0000-0002-0762-6562"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Li","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101865489","display_name":"Zhiping Xia","orcid":"https://orcid.org/0000-0002-1273-5157"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiping Xia","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101907040"],"corresponding_institution_ids":["https://openalex.org/I4210111085"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38880487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1584","last_page":"1589"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":1.0,"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":1.0,"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":0.9998999834060669,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9997000098228455,"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/gamut","display_name":"Gamut","score":0.9346581697463989},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8540775775909424},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.740013062953949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6902891993522644},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6775215268135071},{"id":"https://openalex.org/keywords/color-depth","display_name":"Color depth","score":0.46631675958633423},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.441012978553772},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4337031841278076},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4301162660121918},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42963337898254395},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.34770968556404114},{"id":"https://openalex.org/keywords/dynamic-range","display_name":"Dynamic range","score":0.29124951362609863},{"id":"https://openalex.org/keywords/color-image","display_name":"Color image","score":0.26505202054977417},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2546001672744751},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11009302735328674}],"concepts":[{"id":"https://openalex.org/C2780061478","wikidata":"https://www.wikidata.org/wiki/Q375857","display_name":"Gamut","level":2,"score":0.9346581697463989},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8540775775909424},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.740013062953949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6902891993522644},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6775215268135071},{"id":"https://openalex.org/C91522604","wikidata":"https://www.wikidata.org/wiki/Q690110","display_name":"Color depth","level":5,"score":0.46631675958633423},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.441012978553772},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4337031841278076},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4301162660121918},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42963337898254395},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.34770968556404114},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.29124951362609863},{"id":"https://openalex.org/C142616399","wikidata":"https://www.wikidata.org/wiki/Q5148604","display_name":"Color image","level":4,"score":0.26505202054977417},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2546001672744751},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11009302735328674}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc48107.2020.9148248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc48107.2020.9148248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Wireless Communications and Mobile Computing (IWCMC)","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":21,"referenced_works":["https://openalex.org/W581289835","https://openalex.org/W1522301498","https://openalex.org/W2194775991","https://openalex.org/W2488065443","https://openalex.org/W2524861188","https://openalex.org/W2724213014","https://openalex.org/W2750692136","https://openalex.org/W2798302089","https://openalex.org/W2883638665","https://openalex.org/W2897558784","https://openalex.org/W2911152581","https://openalex.org/W2928842276","https://openalex.org/W2963212406","https://openalex.org/W2963637710","https://openalex.org/W2963861381","https://openalex.org/W2963910742","https://openalex.org/W2969985801","https://openalex.org/W3100927979","https://openalex.org/W4235533043","https://openalex.org/W6631190155","https://openalex.org/W6759073249"],"related_works":["https://openalex.org/W2020582692","https://openalex.org/W2318676702","https://openalex.org/W1963755649","https://openalex.org/W3033884118","https://openalex.org/W2318442036","https://openalex.org/W3092138191","https://openalex.org/W3006819602","https://openalex.org/W4319599328","https://openalex.org/W1978051072","https://openalex.org/W2044382768"],"abstract_inverted_index":{"Featuring":[0],"high":[1,35],"bandwidth":[2],"and":[3,19,26,54,67,97,134,138,149,167,172],"low":[4],"latency,":[5],"the":[6,39,60,70,76,103],"fifth-generation":[7],"(5G)":[8],"network":[9],"can":[10,162],"handle":[11],"Ultra-High":[12],"Definition":[13],"(UHD)":[14],"video":[15,43,106,125,156],"services":[16],"including":[17],"4K":[18],"8K":[20],"with":[21],"High":[22],"Dynamic":[23],"Range":[24],"(HDR)":[25],"Wide":[27],"Color":[28],"Gamut":[29],"(WCG).":[30],"In":[31,85],"order":[32],"to":[33,63,81,101,122],"ensure":[34],"quality":[36,65],"for":[37,93],"consumers,":[38],"parameters":[40,107],"of":[41,117,165],"UHD":[42,105],"contents":[44,157],"such":[45],"as":[46],"resolution,":[47],"bit":[48],"depth,":[49],"frame":[50],"rate,":[51],"color":[52,98,147,170],"gamut":[53,148,171],"etc.":[55],"are":[56,79],"usually":[57],"signaled":[58,113],"during":[59],"whole":[61],"workflow":[62],"keep":[64],"consistency":[66],"interoperability.":[68],"When":[69],"signaling":[71],"is":[72],"unavailable":[73],"or":[74],"unreliable,":[75],"end":[77],"users":[78],"likely":[80],"obtain":[82],"unpleasant":[83],"quality.":[84],"this":[86],"paper,":[87],"we":[88],"propose":[89],"a":[90],"novel":[91],"framework":[92],"CNN":[94],"based":[95],"luminance":[96],"characteristic":[99],"detection":[100],"detect":[102],"actual":[104],"directly":[108],"from":[109],"pixels":[110],"rather":[111],"than":[112],"parameters,":[114],"which":[115],"consists":[116],"two":[118],"image":[119],"classification":[120,175],"networks":[121],"capture":[123],"high-level":[124],"feature":[126],"representation.":[127],"By":[128],"utilizing":[129],"Multi-Class":[130],"Image":[131],"Reference":[132],"(MCIR)":[133],"Local":[135],"Feature":[136],"Extraction":[137],"Fusing":[139],"(LFEF)":[140],"sub-networks,":[141],"our":[142,160],"scheme":[143],"performs":[144],"well":[145],"on":[146,154,169],"dynamic":[150,173],"range":[151,174],"detection.":[152],"Experiments":[153],"practical":[155],"show":[158],"that":[159],"system":[161],"achieve":[163],"accuracy":[164],"99.4%":[166],"94.9%":[168],"separately.":[176]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
