{"id":"https://openalex.org/W3045512887","doi":"https://doi.org/10.1109/icpr48806.2021.9412455","title":"On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures","display_name":"On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3045512887","doi":"https://doi.org/10.1109/icpr48806.2021.9412455","mag":"3045512887"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412455","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412455","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","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/2007.14314","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067037143","display_name":"Matt Poyser","orcid":null},"institutions":[{"id":"https://openalex.org/I190082696","display_name":"Durham University","ror":"https://ror.org/01v29qb04","country_code":"GB","type":"education","lineage":["https://openalex.org/I190082696"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Matt Poyser","raw_affiliation_strings":["Durham University, UK","Durham University,Department of Computer Science,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Durham University, UK","institution_ids":["https://openalex.org/I190082696"]},{"raw_affiliation_string":"Durham University,Department of Computer Science,UK","institution_ids":["https://openalex.org/I190082696"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013030358","display_name":"Amir Atapour\u2013Abarghouei","orcid":"https://orcid.org/0000-0002-4242-4579"},"institutions":[{"id":"https://openalex.org/I190082696","display_name":"Durham University","ror":"https://ror.org/01v29qb04","country_code":"GB","type":"education","lineage":["https://openalex.org/I190082696"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Amir Atapour-Abarghouei","raw_affiliation_strings":["Durham University, UK","Durham University,Department of Computer Science,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Durham University, UK","institution_ids":["https://openalex.org/I190082696"]},{"raw_affiliation_string":"Durham University,Department of Computer Science,UK","institution_ids":["https://openalex.org/I190082696"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045115593","display_name":"Toby P. Breckon","orcid":"https://orcid.org/0000-0003-1666-7590"},"institutions":[{"id":"https://openalex.org/I190082696","display_name":"Durham University","ror":"https://ror.org/01v29qb04","country_code":"GB","type":"education","lineage":["https://openalex.org/I190082696"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Toby P. Breckon","raw_affiliation_strings":["Durham University, UK","Durham University,Department of Computer Science,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Durham University, UK","institution_ids":["https://openalex.org/I190082696"]},{"raw_affiliation_string":"Durham University,Department of Computer Science,UK","institution_ids":["https://openalex.org/I190082696"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I190082696"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2830","last_page":"2837"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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":0.9993000030517578,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987999796867371,"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/computer-science","display_name":"Computer science","score":0.8082490563392639},{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.7860122919082642},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6425384879112244},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.6393808126449585},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5605124831199646},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.5349724292755127},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5275642275810242},{"id":"https://openalex.org/keywords/jpeg-2000","display_name":"JPEG 2000","score":0.4397045969963074},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43170514702796936},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.395283043384552},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38453584909439087},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.27952682971954346},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1662382185459137}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8082490563392639},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.7860122919082642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6425384879112244},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.6393808126449585},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5605124831199646},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.5349724292755127},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5275642275810242},{"id":"https://openalex.org/C69216139","wikidata":"https://www.wikidata.org/wiki/Q931783","display_name":"JPEG 2000","level":5,"score":0.4397045969963074},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43170514702796936},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.395283043384552},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38453584909439087},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.27952682971954346},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1662382185459137}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412455","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412455","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:dro.dur.ac.uk.OAI2:31518","is_oa":false,"landing_page_url":"http://dro.dur.ac.uk/31518/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400188","display_name":"Durham Research Online (Durham University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I190082696","host_organization_name":"Durham University","host_organization_lineage":["https://openalex.org/I190082696"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy, 10-15 January 2021 [Conference proceedings]","raw_type":"Conference item"},{"id":"pmh:oai:arXiv.org:2007.14314","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.14314","pdf_url":"https://arxiv.org/pdf/2007.14314","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3045512887","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2007.14314.pdf","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:durham-repository.worktribe.com:1140363","is_oa":true,"landing_page_url":"https://durham-repository.worktribe.com/output/1140363","pdf_url":null,"source":{"id":"https://openalex.org/S4306400188","display_name":"Durham Research Online (Durham University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I190082696","host_organization_name":"Durham University","host_organization_lineage":["https://openalex.org/I190082696"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Presentation / Conference Contribution"},{"id":"doi:10.48550/arxiv.2007.14314","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2007.14314","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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.14314","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.14314","pdf_url":"https://arxiv.org/pdf/2007.14314","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W639708223","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W2031805124","https://openalex.org/W2037227137","https://openalex.org/W2040903332","https://openalex.org/W2044582964","https://openalex.org/W2097117768","https://openalex.org/W2099563019","https://openalex.org/W2115907784","https://openalex.org/W2116331921","https://openalex.org/W2125458485","https://openalex.org/W2140199336","https://openalex.org/W2146406363","https://openalex.org/W2156303437","https://openalex.org/W2156360858","https://openalex.org/W2163605009","https://openalex.org/W2340897893","https://openalex.org/W2431874326","https://openalex.org/W2559085405","https://openalex.org/W2612445135","https://openalex.org/W2919115771","https://openalex.org/W2941282280","https://openalex.org/W2963881378","https://openalex.org/W2963980515","https://openalex.org/W6600983433","https://openalex.org/W6620707391","https://openalex.org/W6639102338","https://openalex.org/W6639780620","https://openalex.org/W6674914833","https://openalex.org/W6678891163","https://openalex.org/W6682864246","https://openalex.org/W6684191040","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W3088546539","https://openalex.org/W3003173865","https://openalex.org/W3116240515","https://openalex.org/W3004685385","https://openalex.org/W3095331240","https://openalex.org/W3015486533","https://openalex.org/W3147381365","https://openalex.org/W3134376302","https://openalex.org/W2962821904","https://openalex.org/W210475629","https://openalex.org/W3133850457","https://openalex.org/W2970050099","https://openalex.org/W2969260367","https://openalex.org/W2999704753","https://openalex.org/W3021112549","https://openalex.org/W2902993091","https://openalex.org/W3125536987","https://openalex.org/W2890577598","https://openalex.org/W2967116560","https://openalex.org/W2755747701"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,10,84,197],"generalized":[3],"image":[4,51,218],"understanding":[5],"have":[6],"seen":[7],"a":[8,20,73,123,146,166,174,203],"surge":[9],"the":[11,31,42,47,57,66,95,155,223],"use":[12,86],"of":[13,23,34,46,49,59,125,157,171,180,222],"deep":[14,61],"convolutional":[15],"neural":[16],"networks":[17,141],"(CNN)":[18],"across":[19,99],"broad":[21],"range":[22],"image-based":[24],"detection,":[25,109],"classification":[26],"and":[27,52,68,91,112,128,138,148,154,173,211,243],"prediction":[28],"tasks.":[29],"Whilst":[30],"reported":[32],"performance":[33,58,98,153,162,192,231],"these":[35],"approaches":[36],"is":[37,202],"impressive,":[38],"this":[39,119],"study":[40,120],"investigates":[41],"hitherto":[43],"unapproached":[44],"question":[45],"impact":[48,96],"commonplace":[50],"video":[53],"compression":[54,80,159,227],"techniques":[55,81],"on":[56,65,97,186],"such":[60],"learning":[62],"architectures.":[63],"Focusing":[64],"JPEG":[67,167],"H.264":[69,175],"(MPEG-4":[70],"AVC)":[71],"as":[72],"representative":[74],"proxy":[75],"for":[76],"contemporary":[77],"lossy":[78,158,217],"image/video":[79,89,241],"that":[82,213],"are":[83],"common":[85],"within":[87,118,239],"network-connected":[88],"devices":[90,242],"infrastructure,":[92],"we":[93,121],"examine":[94],"five":[100],"discrete":[101],"tasks:":[102],"human":[103],"pose":[104],"estimation,":[105],"semantic":[106],"segmentation,":[107],"object":[108],"action":[110],"recognition,":[111],"monocular":[113],"depth":[114],"estimation.":[115],"As":[116],"such,":[117],"include":[122],"variety":[124],"network":[126,152,191],"architectures":[127,185,206],"domains":[129],"spanning":[130],"end-to-end":[131],"convolution,":[132],"encoder-decoder,":[133],"region-based":[134],"CNN":[135],"(R-CNN),":[136],"dual-stream,":[137],"generative":[139],"adversarial":[140],"(GAN).":[142],"Our":[143],"results":[144],"show":[145],"non-linear":[147],"non-uniform":[149],"relationship":[150,224],"between":[151,205,225],"level":[156,170],"applied.":[160],"Notably,":[161],"decreases":[163],"significantly":[164],"below":[165],"quality":[168],"(quantization)":[169],"15%":[172],"Constant":[176],"Rate":[177],"Factor":[178],"(CRF)":[179],"40.":[181],"However,":[182],"retraining":[183],"said":[184],"pre-compressed":[187],"imagery":[188],"conversely":[189],"recovers":[190],"by":[193],"up":[194],"to":[195,216,228,235],"78.4%":[196],"some":[198],"cases.":[199],"Furthermore,":[200],"there":[201],"correlation":[204],"employing":[207],"an":[208],"encoder-decoder":[209],"pipeline":[210],"those":[212],"demonstrate":[214],"resilience":[215],"compression.":[219],"The":[220],"characteristics":[221],"input":[226],"output":[229],"task":[230],"can":[232],"be":[233],"used":[234],"inform":[236],"design":[237],"decisions":[238],"future":[240],"infrastructure.":[244]},"counts_by_year":[{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}