{"id":"https://openalex.org/W7131269967","doi":"https://doi.org/10.1109/vcip67698.2025.11396825","title":"Towards Efficient Neuromorphic Data Processing: A Novel Representation with Lossless Spatio-Temporal Compression","display_name":"Towards Efficient Neuromorphic Data Processing: A Novel Representation with Lossless Spatio-Temporal Compression","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W7131269967","doi":"https://doi.org/10.1109/vcip67698.2025.11396825"},"language":null,"primary_location":{"id":"doi:10.1109/vcip67698.2025.11396825","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","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/A5015909657","display_name":"Sally Khaidem","orcid":"https://orcid.org/0000-0001-5262-6724"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sally Khaidem","raw_affiliation_strings":["IIT Madras,Dept. of Electrical Engineering,Chennai,India,600036"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Madras,Dept. of Electrical Engineering,Chennai,India,600036","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126735066","display_name":"Akshay Dharmaraj C","orcid":null},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Akshay Dharmaraj C","raw_affiliation_strings":["IIT Madras,Dept. of Engineering Physics,Chennai,India,600036"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIT Madras,Dept. of Engineering Physics,Chennai,India,600036","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126744906","display_name":"Mansi Sharma","orcid":null},"institutions":[{"id":"https://openalex.org/I162030827","display_name":"Thapar Institute of Engineering & Technology","ror":"https://ror.org/00wdq3744","country_code":"IN","type":"education","lineage":["https://openalex.org/I162030827"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mansi Sharma","raw_affiliation_strings":["Thapar Institute of Engineering and Technology,Dept. of Computer Science and Engineering,Patiala,India,147004"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thapar Institute of Engineering and Technology,Dept. of Computer Science and Engineering,Patiala,India,147004","institution_ids":["https://openalex.org/I162030827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72812206,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.3894999921321869,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.3894999921321869,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.14229999482631683,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.07320000231266022,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.8223000168800354},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5860000252723694},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5760999917984009},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4934999942779541},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.4846000075340271},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4542999863624573},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4447999894618988},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.44369998574256897},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.4339999854564667}],"concepts":[{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.8223000168800354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7447999715805054},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5860000252723694},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5760999917984009},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4934999942779541},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.4846000075340271},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4542999863624573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4514000117778778},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4447999894618988},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.44369998574256897},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.4339999854564667},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3871999979019165},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35899999737739563},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3400000035762787},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C1769480","wikidata":"https://www.wikidata.org/wiki/Q1345239","display_name":"Entropy encoding","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C57890076","wikidata":"https://www.wikidata.org/wiki/Q4680725","display_name":"Adaptive coding","level":4,"score":0.2962999939918518},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.2946000099182129},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.2757999897003174},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25859999656677246},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip67698.2025.11396825","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W206948248","https://openalex.org/W2085880494","https://openalex.org/W2303422468","https://openalex.org/W2493355792","https://openalex.org/W2567239141","https://openalex.org/W2796402180","https://openalex.org/W2883647873","https://openalex.org/W2886377182","https://openalex.org/W2903638214","https://openalex.org/W2919630283","https://openalex.org/W3175067038","https://openalex.org/W4289825123","https://openalex.org/W4383346294","https://openalex.org/W4391408888","https://openalex.org/W4402031126","https://openalex.org/W4404577170"],"related_works":[],"abstract_inverted_index":{"Dynamic":[0],"Vision":[1],"Sensors":[2],"(DVS)":[3],"asynchronously":[4],"capture":[5],"brightness":[6],"changes":[7],"at":[8,127],"pixel-level":[9],"precision,":[10],"enabling":[11],"high":[12],"temporal":[13,65,102,129],"resolution,":[14],"wide":[15],"dynamic":[16,165],"range":[17],"and":[18,49,67,157,166],"low":[19],"power":[20],"consumption.":[21],"To":[22],"sustainably":[23],"manage":[24],"redundancy":[25],"in":[26],"event":[27],"data,":[28],"we":[29],"introduce":[30],"a":[31,57,92,170],"novel":[32],"lossless":[33,176],"compression":[34,106,142,177],"framework":[35],"that":[36],"exploits":[37],"DVS-specific":[38],"characteristics":[39],"through":[40,91],"two":[41],"core":[42],"representations:":[43],"the":[44,50,62,161],"Super":[45],"Binary":[46],"Map":[47],"(SBM)":[48],"Temporal":[51],"Event":[52],"Vector":[53],"(TEV).":[54],"SBM":[55],"is":[56],"voxelized":[58],"binary":[59],"structure":[60],"capturing":[61],"inherent":[63],"spatio":[64,101],"sparsity":[66],"polarity":[68],"of":[69,178],"events,":[70],"compressed":[71],"effectively":[72],"using":[73],"Run-Length":[74],"Encoding":[75],"(RLE).":[76],"TEV":[77],"complements":[78],"this":[79],"by":[80,97],"precisely":[81],"encoding":[82],"each":[83],"event\u2019s":[84],"timing":[85],"into":[86],"compact,":[87],"variable-length":[88],"vectors,":[89],"optimized":[90],"context-adaptive":[93],"entropy":[94],"coder":[95],"inspired":[96],"Markov":[98],"models.":[99],"This":[100,155],"representation":[103],"substantially":[104],"enhances":[105],"efficiency.":[107],"Our":[108],"approach":[109],"significantly":[110],"outperforms":[111],"conventional":[112],"methods,":[113],"achieving":[114],"improvements":[115],"up":[116],"to":[117,145,164],"52\u00d7":[118],"over":[119,122,125],"AVC,":[120],"32\u00d7":[121],"HEVC,":[123],"and1094\u00d7s":[124],"VVC":[126],"fine":[128],"scales.":[130],"At":[131],"ultra-fine":[132],"granularity":[133],"(":[134],"or":[135],"10,000":[136],"fps),":[137],"our":[138],"method":[139],"attains":[140],"remarkable":[141],"ratios":[143],"(up":[144],"307\u00d7),":[146],"vastly":[147],"exceeding":[148],"traditional":[149],"approaches":[150],"(e.g.,":[151],"LZMA,":[152],"Brotli,":[153],"Zlib).":[154],"consistent":[156],"substantial":[158],"margin":[159],"highlights":[160],"method\u2019s":[162],"adaptability":[163],"static":[167],"scenes,":[168],"offering":[169],"scalable,":[171],"domain-specific":[172],"solution":[173],"for":[174],"efficient":[175],"event-based":[179],"vision":[180],"data.":[181]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-25T00:00:00"}
