{"id":"https://openalex.org/W2919080262","doi":"https://doi.org/10.1109/tpami.2019.2903062","title":"Learning Raw Image Reconstruction-Aware Deep Image Compressors","display_name":"Learning Raw Image Reconstruction-Aware Deep Image Compressors","publication_year":2019,"publication_date":"2019-03-04","ids":{"openalex":"https://openalex.org/W2919080262","doi":"https://doi.org/10.1109/tpami.2019.2903062","mag":"2919080262","pmid":"https://pubmed.ncbi.nlm.nih.gov/30843804"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2019.2903062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2903062","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5051631051","display_name":"Abhijith Punnappurath","orcid":"https://orcid.org/0000-0003-3438-5896"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Abhijith Punnappurath","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, York University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106406020","display_name":"Michael S. Brown","orcid":"https://orcid.org/0000-0002-9840-0795"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Michael S. Brown","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, York University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051631051"],"corresponding_institution_ids":["https://openalex.org/I192455969"],"apc_list":null,"apc_paid":null,"fwci":2.6318,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.92032458,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"42","issue":"4","first_page":"1013","last_page":"1019"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9994000196456909,"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/T11019","display_name":"Image Enhancement 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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7855032682418823},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7716515064239502},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.7022274732589722},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6412799954414368},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6158051490783691},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.545021116733551},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5162882208824158},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.505035936832428},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3445179760456085},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2853032946586609},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27339571714401245}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7855032682418823},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7716515064239502},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.7022274732589722},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6412799954414368},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6158051490783691},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.545021116733551},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5162882208824158},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.505035936832428},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3445179760456085},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2853032946586609},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27339571714401245}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2019.2903062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2903062","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:30843804","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30843804","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320326644","display_name":"Canada First Research Excellence Fund","ror":null},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W18175933","https://openalex.org/W1522301498","https://openalex.org/W1580389772","https://openalex.org/W2040057137","https://openalex.org/W2069281566","https://openalex.org/W2093580403","https://openalex.org/W2110288559","https://openalex.org/W2118217749","https://openalex.org/W2127701282","https://openalex.org/W2130700878","https://openalex.org/W2140196014","https://openalex.org/W2143736264","https://openalex.org/W2209359034","https://openalex.org/W2343938449","https://openalex.org/W2460242234","https://openalex.org/W2469102383","https://openalex.org/W2519963891","https://openalex.org/W2552465432","https://openalex.org/W2593493485","https://openalex.org/W2597747080","https://openalex.org/W2604392022","https://openalex.org/W2732044853","https://openalex.org/W2785562966","https://openalex.org/W2799228606","https://openalex.org/W2962676454","https://openalex.org/W2962790638","https://openalex.org/W2962891349","https://openalex.org/W2963147844","https://openalex.org/W2963149687","https://openalex.org/W2963359792","https://openalex.org/W2963449488","https://openalex.org/W2964098744","https://openalex.org/W2964121744","https://openalex.org/W2964164354","https://openalex.org/W4294567867","https://openalex.org/W4297659253","https://openalex.org/W6631190155","https://openalex.org/W6694251005","https://openalex.org/W6704734575","https://openalex.org/W6734035190","https://openalex.org/W6735443624","https://openalex.org/W6736182379","https://openalex.org/W6738494155","https://openalex.org/W6741057705"],"related_works":["https://openalex.org/W3162084246","https://openalex.org/W4386290264","https://openalex.org/W2021203468","https://openalex.org/W2129829718","https://openalex.org/W2521595930","https://openalex.org/W4243608781","https://openalex.org/W3165542721","https://openalex.org/W4313046148","https://openalex.org/W4378191574","https://openalex.org/W2161981399"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1,23],"image":[2,14,29,50,61,75,102,127,133],"compressors":[3,103],"are":[4],"actively":[5],"being":[6],"explored":[7],"in":[8,32,144,192],"an":[9],"effort":[10],"to":[11,104,129,173,181,189],"supersede":[12],"conventional":[13,82],"compression":[15,24,128],"algorithms,":[16],"such":[17],"as":[18,205],"JPEG.":[19],"Conventional":[20],"and":[21,136,159],"deep":[22,101,124],"algorithms":[25],"focus":[26],"on":[27,80,202],"minimizing":[28],"fidelity":[30,134,168,204],"errors":[31,135],"the":[33,46,58,73,81,89,98,108,148,195],"nonlinear":[34],"standard":[35],"RGB":[36],"(sRGB)":[37],"color":[38],"space.":[39],"However,":[40,77],"for":[41,166],"many":[42],"computer":[43],"vision":[44],"tasks,":[45],"sensor's":[47],"linear":[48],"raw-RGB":[49,60,90],"is":[51,86,150,170],"desirable.":[52],"Recent":[53],"work":[54],"has":[55],"shown":[56],"that":[57,85,122],"original":[59],"can":[62],"be":[63,105],"reconstructed":[64],"using":[65,154],"only":[66,165,199],"small":[67],"amounts":[68],"of":[69,88,100,107,111,194],"metadata":[70],"embedded":[71],"inside":[72],"JPEG":[74,83],"[1].":[76],"[1]":[78],"relied":[79],"encoding":[84],"unaware":[87],"reconstruction":[91,138,177,197],"task.":[92],"In":[93],"this":[94,115,142],"paper,":[95],"we":[96,117],"examine":[97],"ability":[99],"\"aware\"":[106],"additional":[109],"objective":[110],"raw":[112,137,176,196],"reconstruction.":[113],"Towards":[114],"goal,":[116],"describe":[118,141],"a":[119,161],"general":[120],"framework":[121],"enables":[123],"networks":[125],"targeting":[126],"jointly":[130],"consider":[131],"both":[132],"errors.":[139],"We":[140],"approach":[143],"two":[145],"scenarios:":[146],"(1)":[147],"network":[149,162],"trained":[151,164],"from":[152],"scratch":[153],"our":[155,175,185],"proposed":[156],"joint":[157],"loss,":[158],"(2)":[160],"originally":[163],"sRGB":[167,182,203],"loss":[169,187],"later":[171],"fine-tuned":[172],"incorporate":[174],"loss.":[178],"When":[179],"compared":[180],"fidelity-only":[183],"compression,":[184],"combined":[186],"leads":[188],"appreciable":[190],"improvements":[191],"PSNR":[193],"with":[198],"minor":[200],"impact":[201],"measured":[206],"by":[207],"MS-SSIM.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}