{"id":"https://openalex.org/W4312538735","doi":"https://doi.org/10.1109/iscas48785.2022.9937673","title":"CNN-based Partitioning Structure Prediction for VVC Intra Speedup: Bottom-Up-based and Top-Down-based","display_name":"CNN-based Partitioning Structure Prediction for VVC Intra Speedup: Bottom-Up-based and Top-Down-based","publication_year":2022,"publication_date":"2022-05-28","ids":{"openalex":"https://openalex.org/W4312538735","doi":"https://doi.org/10.1109/iscas48785.2022.9937673"},"language":"en","primary_location":{"id":"doi:10.1109/iscas48785.2022.9937673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas48785.2022.9937673","pdf_url":null,"source":{"id":"https://openalex.org/S4363604393","display_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5100387803","display_name":"Yue Li","orcid":"https://orcid.org/0000-0002-5624-7235"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yue Li","raw_affiliation_strings":["Bytedance Inc.8910 University Center Lane,San Diego,CA,USA,92122"],"affiliations":[{"raw_affiliation_string":"Bytedance Inc.8910 University Center Lane,San Diego,CA,USA,92122","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425726","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0003-2118-4876"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Bytedance Inc.8910 University Center Lane,San Diego,CA,USA,92122"],"affiliations":[{"raw_affiliation_string":"Bytedance Inc.8910 University Center Lane,San Diego,CA,USA,92122","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101727196","display_name":"Jizheng Xu","orcid":"https://orcid.org/0009-0005-4563-6787"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jizheng Xu","raw_affiliation_strings":["Bytedance Inc.8910 University Center Lane,San Diego,CA,USA,92122"],"affiliations":[{"raw_affiliation_string":"Bytedance Inc.8910 University Center Lane,San Diego,CA,USA,92122","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100387803"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3677,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53175355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1953","last_page":"1957"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10741","display_name":"Video Coding and Compression Technologies","score":0.9998999834060669,"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":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9976999759674072,"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":0.7733012437820435},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.6643491387367249},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6567909121513367},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6357755661010742},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6183371543884277},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5616241097450256},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5497187376022339},{"id":"https://openalex.org/keywords/algorithmic-efficiency","display_name":"Algorithmic efficiency","score":0.5268836617469788},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.45548903942108154},{"id":"https://openalex.org/keywords/block-code","display_name":"Block code","score":0.4317566156387329},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.426936537027359},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.24701091647148132},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20213431119918823},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13283619284629822},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09166207909584045}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7733012437820435},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.6643491387367249},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6567909121513367},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6357755661010742},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6183371543884277},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5616241097450256},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5497187376022339},{"id":"https://openalex.org/C116709606","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Algorithmic efficiency","level":3,"score":0.5268836617469788},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.45548903942108154},{"id":"https://openalex.org/C157125643","wikidata":"https://www.wikidata.org/wiki/Q884707","display_name":"Block code","level":3,"score":0.4317566156387329},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.426936537027359},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.24701091647148132},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20213431119918823},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13283619284629822},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09166207909584045},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas48785.2022.9937673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas48785.2022.9937673","pdf_url":null,"source":{"id":"https://openalex.org/S4363604393","display_name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","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":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2146395539","https://openalex.org/W2194775991","https://openalex.org/W2276329208","https://openalex.org/W2739757502","https://openalex.org/W2791620681","https://openalex.org/W2921369765","https://openalex.org/W2987291011","https://openalex.org/W2990674010","https://openalex.org/W3037841842","https://openalex.org/W3090851217","https://openalex.org/W3160130949","https://openalex.org/W4295312788","https://openalex.org/W6694662063","https://openalex.org/W6760066971","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2548909300","https://openalex.org/W2391322781","https://openalex.org/W2594233795","https://openalex.org/W2185045962","https://openalex.org/W2529305063","https://openalex.org/W1980294463","https://openalex.org/W2235484757","https://openalex.org/W2081549790"],"abstract_inverted_index":{"Versatile":[0],"Video":[1,16],"Coding":[2,17],"(VVC)":[3],"is":[4,60,101,126],"capable":[5],"of":[6,40,53,58,98,112,137,154,206],"achieving":[7],"approximately":[8],"25%":[9],"bitrate":[10],"reduction":[11,175,191],"compared":[12],"with":[13,78,176,192],"High":[14],"Efficiency":[15],"(HEVC)":[18],"at":[19],"the":[20,31,37,54,63,75,86,95,109,118,122,134,165,181,198],"same":[21],"objective":[22],"quality":[23],"under":[24],"all":[25],"intra":[26],"configuration.":[27],"Meanwhile,":[28],"VVC":[29,48],"sacrifices":[30],"encoding":[32,38,173,189],"complexity":[33,59,174,190,210],"by":[34],"26":[35],"times":[36],"time":[39],"HEVC,":[41],"which":[42],"makes":[43],"it":[44],"impractical":[45],"to":[46,62,107,132],"use":[47],"without":[49],"optimization.":[50],"In":[51,163,196],"view":[52],"fact":[55],"that":[56],"most":[57],"due":[61],"novel":[64],"block":[65],"partitioning":[66,76,87,110,124,135],"structure":[67,77,111,136],"in":[68,121,204],"VVC,":[69],"this":[70],"paper":[71],"focuses":[72],"on":[73,152,170,186],"predicting":[74],"convolutional":[79,147],"neural":[80,148],"networks.":[81,149],"Specifically,":[82],"we":[83,142],"first":[84,102,127],"formulate":[85],"prediction":[88],"problem":[89],"into":[90],"two":[91],"alternatives:":[92],"bottom-up-based":[93,166],"where":[94,117],"split":[96],"type":[97],"subblock":[99],"boundaries":[100],"predicted":[103],"and":[104,129,209],"then":[105,130],"used":[106,131],"infer":[108],"each":[113,138],"coding":[114,139,207],"unit,":[115],"top-down-based":[116,182],"probability":[119],"distribution":[120],"ensemble":[123],"space":[125],"derived":[128],"decide":[133],"unit.":[140],"Then,":[141],"address":[143],"both":[144],"formulations":[145],"using":[146],"When":[150],"evaluating":[151],"top":[153],"VTM7.0,":[155],"proposed":[156,199],"schemes":[157,200],"perform":[158],"favorably":[159],"against":[160],"state-of-the-art":[161],"works.":[162],"particular,":[164],"method":[167,183],"can":[168,201],"bring":[169],"average":[171,187],"52.3%":[172],"0.46%":[177],"BD-rate":[178,194],"increase":[179],"while":[180],"could":[184],"provide":[185],"39.5%":[188],"0.28%":[193],"increase.":[195],"addition,":[197],"offer":[202],"scalability":[203],"terms":[205],"efficiency":[208],"trade-off.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
