{"id":"https://openalex.org/W4415164143","doi":"https://doi.org/10.48550/arxiv.2506.12896","title":"Structure-Preserving Patch Decoding for Efficient Neural Video Representation","display_name":"Structure-Preserving Patch Decoding for Efficient Neural Video Representation","publication_year":2025,"publication_date":"2025-06-15","ids":{"openalex":"https://openalex.org/W4415164143","doi":"https://doi.org/10.48550/arxiv.2506.12896"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2506.12896","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.12896","pdf_url":"https://arxiv.org/pdf/2506.12896","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.12896","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113652267","display_name":"T. Hayami","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hayami, Taiga","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113807392","display_name":"K. Koizumi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koizumi, Kakeru","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088389427","display_name":"Hiroshi Watanabe","orcid":"https://orcid.org/0000-0002-6742-1913"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Watanabe, Hiroshi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113652267"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9940000176429749,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9940000176429749,"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/T10320","display_name":"Neural Networks and Applications","score":0.972599983215332,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9692000150680542,"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/decoding-methods","display_name":"Decoding methods","score":0.6473000049591064},{"id":"https://openalex.org/keywords/classification-of-discontinuities","display_name":"Classification of discontinuities","score":0.598800003528595},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5070000290870667},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4652999937534332},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4424000084400177},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4153999984264374},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.40560001134872437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7422000169754028},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6473000049591064},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.628000020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6092000007629395},{"id":"https://openalex.org/C15627037","wikidata":"https://www.wikidata.org/wiki/Q541961","display_name":"Classification of discontinuities","level":2,"score":0.598800003528595},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5070000290870667},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4424000084400177},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4153999984264374},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.40560001134872437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C172849965","wikidata":"https://www.wikidata.org/wiki/Q3148875","display_name":"Reference frame","level":3,"score":0.3758000135421753},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3188000023365021},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C57654395","wikidata":"https://www.wikidata.org/wiki/Q1097775","display_name":"Compression artifact","level":5,"score":0.2563000023365021},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2506.12896","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.12896","pdf_url":"https://arxiv.org/pdf/2506.12896","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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.2506.12896","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.12896","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2506.12896","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.12896","pdf_url":"https://arxiv.org/pdf/2506.12896","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":[1,95],"representations":[2],"(INRs)":[3],"are":[4],"the":[5,68,132,142,156],"subject":[6],"of":[7,70,117],"extensive":[8],"research,":[9],"particularly":[10],"in":[11],"their":[12],"application":[13],"to":[14,24,33,59,84,104,127,144],"modeling":[15],"complex":[16],"signals":[17],"by":[18],"mapping":[19,30],"spatial":[20,49,81,90,134],"and":[21,54,160,169,188],"temporal":[22],"coordinates":[23],"corresponding":[25],"values.":[26],"When":[27],"handling":[28],"videos,":[29],"compact":[31],"inputs":[32],"entire":[34,64],"frames":[35,65,120],"or":[36],"spatially":[37,118],"partitioned":[38],"patch":[39,115],"images":[40,116],"is":[41],"an":[42],"effective":[43],"approach.":[44],"This":[45,129,164],"strategy":[46,153],"better":[47,189],"preserves":[48,131],"relationships,":[50],"reduces":[51,166],"computational":[52],"overhead,":[53],"improves":[55],"reconstruction":[56,69,186],"quality":[57,187],"compared":[58],"coordinate-based":[60],"mapping.":[61],"However,":[62],"predicting":[63],"often":[66],"limits":[67],"high-frequency":[71],"visual":[72],"details.":[73,163],"Additionally,":[74],"conventional":[75],"patch-based":[76],"approaches":[77],"based":[78,99],"on":[79,100,176],"uniform":[80],"partitioning":[82],"tend":[83],"introduce":[85],"boundary":[86,167],"discontinuities":[87],"that":[88,154,181],"degrade":[89],"coherence.":[91],"We":[92,140],"propose":[93],"a":[94,122,150],"video":[96,112,178],"representation":[97],"method":[98,109,183],"Structure-Preserving":[101],"Patches":[102],"(SPPs)":[103],"address":[105],"such":[106],"limitations.":[107],"Our":[108],"separates":[110],"each":[111],"frame":[113],"into":[114],"aligned":[119],"through":[121],"deterministic":[123],"pixel-based":[124],"splitting":[125],"similar":[126],"PixelUnshuffle.":[128],"operation":[130],"global":[133,157],"structure":[135],"while":[136],"allowing":[137],"patch-level":[138],"decoding.":[139],"train":[141],"decoder":[143],"reconstruct":[145],"these":[146],"structured":[147],"patches,":[148],"enabling":[149],"global-to-local":[151],"decoding":[152],"captures":[155],"layout":[158],"first":[159],"refines":[161],"local":[162],"effectively":[165],"artifacts":[168],"mitigates":[170],"distortions":[171],"from":[172],"naive":[173],"upsampling.":[174],"Experiments":[175],"standard":[177],"datasets":[179],"demonstrate":[180],"our":[182],"achieves":[184],"higher":[185],"compression":[190],"performance":[191],"than":[192],"existing":[193],"INR-based":[194],"baselines.":[195]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-14T00:00:00"}