{"id":"https://openalex.org/W3035685239","doi":"https://doi.org/10.1109/icme46284.2020.9102843","title":"An Emerging Coding Paradigm Vcm: A Scalable Coding Approach Beyond Feature And Signal","display_name":"An Emerging Coding Paradigm Vcm: A Scalable Coding Approach Beyond Feature And Signal","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3035685239","doi":"https://doi.org/10.1109/icme46284.2020.9102843","mag":"3035685239"},"language":"en","primary_location":{"id":"doi:10.1109/icme46284.2020.9102843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102843","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 Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5054386013","display_name":"Sifeng Xia","orcid":"https://orcid.org/0000-0003-0301-0004"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sifeng Xia","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090187565","display_name":"Kunchangtai Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kunchangtai Liang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070884682","display_name":"Wenhan Yang","orcid":"https://orcid.org/0000-0002-1692-0069"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhan Yang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024879728","display_name":"Ling\u2010Yu Duan","orcid":"https://orcid.org/0000-0002-4491-2023"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling-Yu Duan","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100761525","display_name":"Jiaying Liu","orcid":"https://orcid.org/0000-0002-0468-9576"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaying Liu","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":1.5378,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.88614028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T10531","display_name":"Advanced Vision and Imaging","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/T11105","display_name":"Advanced Image Processing 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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8127502202987671},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6681384444236755},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5731582045555115},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5144691467285156},{"id":"https://openalex.org/keywords/codec","display_name":"Codec","score":0.48427242040634155},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4547407627105713},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4387891888618469},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4363194406032562},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.4219711720943451},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4171789586544037},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3830937147140503},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37991970777511597},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3118751347064972}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8127502202987671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6681384444236755},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5731582045555115},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5144691467285156},{"id":"https://openalex.org/C161765866","wikidata":"https://www.wikidata.org/wiki/Q184748","display_name":"Codec","level":2,"score":0.48427242040634155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4547407627105713},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4387891888618469},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4363194406032562},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.4219711720943451},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4171789586544037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3830937147140503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37991970777511597},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3118751347064972},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme46284.2020.9102843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102843","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 Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2140199336","https://openalex.org/W2146395539","https://openalex.org/W2307035320","https://openalex.org/W2477177239","https://openalex.org/W2556782416","https://openalex.org/W2749544970","https://openalex.org/W2765433083","https://openalex.org/W2774625825","https://openalex.org/W2883938080","https://openalex.org/W2901921200","https://openalex.org/W2913664580","https://openalex.org/W2950568498","https://openalex.org/W2952587893","https://openalex.org/W2963003152","https://openalex.org/W2963168844","https://openalex.org/W2963782415","https://openalex.org/W2963800363","https://openalex.org/W2964121744","https://openalex.org/W2970935033","https://openalex.org/W2984529706","https://openalex.org/W3082548248","https://openalex.org/W4320013936","https://openalex.org/W6631190155","https://openalex.org/W6698200468","https://openalex.org/W6730028046","https://openalex.org/W6772610713"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W2280377497","https://openalex.org/W3174044702","https://openalex.org/W4238433571","https://openalex.org/W2967848559","https://openalex.org/W4283803360","https://openalex.org/W4317695495","https://openalex.org/W4387506531"],"abstract_inverted_index":{"In":[0],"this":[1,59],"paper,":[2],"we":[3,61,106],"study":[4],"a":[5,52,92,102,108,132,150,218],"new":[6],"problem":[7],"arising":[8],"from":[9],"the":[10,24,39,66,118,135,139,144,155,158,163,192],"emerging":[11],"MPEG":[12],"standardization":[13],"effort":[14],"Video":[15],"Coding":[16],"for":[17,45,78,172,224],"Machine":[18],"(VCM)1,":[19],"which":[20,87,216],"aims":[21],"to":[22,37,73,94,113,126,142],"bridge":[23,93],"gap":[25],"between":[26],"visual":[27,88],"feature":[28,140],"compression":[29,76],"and":[30,48,70,81,97,169,227],"classical":[31],"video":[32,115,194],"coding.":[33],"VCM":[34],"is":[35,167],"committed":[36],"address":[38],"requirement":[40],"of":[41,68,120,146,157,221],"compact":[42,99,168],"signal":[43,223],"representation":[44,141],"both":[46,79,225],"machine":[47,80,228],"human":[49,82,226],"vision":[50,83,174],"in":[51,64,86,101,198,213],"more":[53],"or":[54],"less":[55],"scalable":[56,103],"way.":[57],"To":[58],"end,":[60],"make":[62],"endeavors":[63],"leveraging":[65],"strength":[67],"predictive":[69,133],"generative":[71,151],"models":[72],"support":[74],"advanced":[75],"techniques":[77],"tasks":[84],"simultaneously,":[85],"features":[89],"serve":[90],"as":[91,200,202],"connect":[95],"signal-level":[96],"task-level":[98],"representations":[100],"manner.":[104],"Specifically,":[105],"employ":[107],"conditional":[109],"deep":[110],"generation":[111],"network":[112,136],"reconstruct":[114],"frames":[116,148],"with":[117,191],"guidance":[119],"learned":[121],"motion":[122,129,165],"pattern.":[123],"By":[124],"learning":[125],"extract":[127],"sparse":[128,164],"pattern":[130,166],"via":[131,149],"model,":[134,152],"elegantly":[137],"leverages":[138],"generate":[143],"appearance":[145,156],"to-be-coded":[147],"relying":[153],"on":[154],"coded":[159],"key":[160],"frames.":[161],"Meanwhile,":[162],"highly":[170,208],"effective":[171],"high-level":[173],"tasks,":[175],"e.g.":[176],"action":[177,204],"recognition.":[178],"Experimental":[179],"results":[180],"demonstrate":[181],"that":[182],"our":[183],"method":[184],"yields":[185],"much":[186],"better":[187],"reconstruction":[188],"quality":[189],"compared":[190],"traditional":[193],"codecs":[195],"(0.0063":[196],"gain":[197,212],"SSIM),":[199],"well":[201],"state-of-the-art":[203],"recognition":[205,214],"performance":[206],"over":[207],"compressed":[209],"videos":[210],"(9.4%":[211],"accuracy),":[215],"showcases":[217],"promising":[219],"paradigm":[220],"coding":[222],"vision.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
