{"id":"https://openalex.org/W4416526510","doi":"https://doi.org/10.1109/wacv61042.2026.00078","title":"How to Design and Train Your Implicit Neural Representation for Video Compression","display_name":"How to Design and Train Your Implicit Neural Representation for Video Compression","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W4416526510","doi":"https://doi.org/10.1109/wacv61042.2026.00078"},"language":"en","primary_location":{"id":"doi:10.1109/wacv61042.2026.00078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.24127","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031894535","display_name":"Matthew Gwilliam","orcid":"https://orcid.org/0000-0001-9826-6285"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Gwilliam","raw_affiliation_strings":["University of Maryland,College Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065755128","display_name":"Roy Zhang","orcid":"https://orcid.org/0000-0001-9962-7951"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roy Zhang","raw_affiliation_strings":["University of Maryland,College Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088212289","display_name":"Namitha Padmanabhan","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Namitha Padmanabhan","raw_affiliation_strings":["University of Maryland,College Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068782412","display_name":"Hongyang Du","orcid":"https://orcid.org/0000-0002-8220-6525"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongyang Du","raw_affiliation_strings":["University of Maryland,College Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101614445","display_name":"Abhinav Shrivastava","orcid":"https://orcid.org/0000-0003-0758-4638"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Shrivastava","raw_affiliation_strings":["University of Maryland,College Park"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,College Park","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02252587,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"729","last_page":"739"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.5792999863624573,"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":0.5792999863624573,"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":0.15970000624656677,"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":0.061799999326467514,"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/encoding","display_name":"Encoding (memory)","score":0.7236999869346619},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5663999915122986},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5663999915122986},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.5027999877929688},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4876999855041504},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47429999709129333},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.40299999713897705},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.38909998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732999920845032},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.7236999869346619},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5663999915122986},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5663999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5358999967575073},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5027999877929688},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4876999855041504},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47429999709129333},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.35429999232292175},{"id":"https://openalex.org/C106030495","wikidata":"https://www.wikidata.org/wiki/Q1797012","display_name":"Video compression picture types","level":4,"score":0.3522999882698059},{"id":"https://openalex.org/C103910844","wikidata":"https://www.wikidata.org/wiki/Q2631256","display_name":"Video quality","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33160001039505005},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.3292999863624573},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3181999921798706},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29600000381469727},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.25929999351501465}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/wacv61042.2026.00078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2506.24127","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.24127","pdf_url":"https://arxiv.org/pdf/2506.24127","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},{"id":"doi:10.48550/arxiv.2506.24127","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.24127","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2506.24127","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.24127","pdf_url":"https://arxiv.org/pdf/2506.24127","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Implicit":[0],"neural":[1],"representation":[2],"(INR)":[3],"methods":[4,36,57,99],"for":[5,27,40,81,110,128,163,179,207],"video":[6,83,130,154],"compression":[7,14],"have":[8],"recently":[9],"achieved":[10],"visual":[11],"quality":[12,182],"and":[13,52,86,191,200,215],"ratios":[15],"that":[16],"are":[17,37,100],"competitive":[18],"with":[19,212],"traditional":[20],"pipelines.":[21],"However,":[22],"due":[23],"to":[24,47,50,106,124,156,161,177,188,210],"the":[25,31,54,59,125,138,145,169,172,197],"need":[26],"per-sample":[28],"network":[29],"training,":[30],"encoding":[32,139,160],"speeds":[33],"of":[34,56,67,91,147,171],"these":[35,92],"too":[38],"slow":[39],"practical":[41],"adoption.":[42],"We":[43,78,135,166],"develop":[44],"a":[45,88],"library":[46],"allow":[48,162,178],"us":[49],"disentangle":[51,157],"review":[53],"components":[55],"from":[58,153,159],"NeRV":[60,95,108,133],"family,":[61],"reframing":[62],"their":[63],"performance":[64],"in":[65,131,185],"terms":[66],"not":[68],"only":[69],"size-quality":[70],"trade-offs,":[71],"but":[72],"also":[73],"impacts":[74],"on":[75,121,196],"training":[76,103,158,176],"time.":[77],"uncover":[79],"principles":[80],"effective":[82],"INR":[84,151,174],"design":[85],"propose":[87,167],"state-of-the-art":[89],"configuration":[90],"components,":[93],"Rabbit":[94],"(RNeRV).":[96],"When":[97],"all":[98],"given":[101],"equal":[102,213],"time":[104],"(equivalent":[105],"300":[107],"epochs)":[109],"7":[111],"different":[112],"UVG":[113],"videos":[114],"at":[115,193,222],"1080p,":[116],"RNeRV":[117],"achieves":[118],"+1.27%":[119],"PSNR":[120,190],"average":[122],"compared":[123],"best-performing":[126],"alternative":[127],"each":[129],"our":[132],"library.":[134],"then":[136],"tackle":[137],"speed":[140],"issue":[141],"head-on":[142],"by":[143,205],"investigating":[144],"viability":[146],"hyper-networks,":[148],"which":[149],"predict":[150],"weights":[152,170],"inputs,":[155],"real-time":[164],"encoding.":[165],"masking":[168],"predicted":[173],"during":[175],"variable,":[180],"higher":[181],"compression,":[183],"resulting":[184],"1.7%":[186],"improvements":[187,209],"both":[189],"MS-SSIM":[192],"0.037":[194],"bpp":[195,214],"UCF-101":[198],"dataset,":[199],"we":[201],"increase":[202],"hyper-network":[203],"parameters":[204],"0.4%":[206],"2.5%/2.7%":[208],"PSNR/MS-SSIM":[211],"similar":[216],"speeds.":[217],"Our":[218],"code":[219],"is":[220],"available":[221],"https://github.com/mgwillia/vinrb.":[223]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}