{"id":"https://openalex.org/W4360605078","doi":"https://doi.org/10.1109/icaiic57133.2023.10067012","title":"Noise-cuts-Noise Approach for Mitigating the JPEG Distortions in Deep Learning","display_name":"Noise-cuts-Noise Approach for Mitigating the JPEG Distortions in Deep Learning","publication_year":2023,"publication_date":"2023-02-20","ids":{"openalex":"https://openalex.org/W4360605078","doi":"https://doi.org/10.1109/icaiic57133.2023.10067012"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic57133.2023.10067012","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaiic57133.2023.10067012","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"},"type":"article","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/A5077748142","display_name":"Ijaz Ahmad","orcid":"https://orcid.org/0000-0002-6022-7413"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Ijaz Ahmad","raw_affiliation_strings":["Chosun University,Department of Computer Engineering,Gwangju,Korea,61452"],"affiliations":[{"raw_affiliation_string":"Chosun University,Department of Computer Engineering,Gwangju,Korea,61452","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009170109","display_name":"Seokjoo Shin","orcid":"https://orcid.org/0000-0003-2092-1336"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seokjoo Shin","raw_affiliation_strings":["Chosun University,Department of Computer Engineering,Gwangju,Korea,61452"],"affiliations":[{"raw_affiliation_string":"Chosun University,Department of Computer Engineering,Gwangju,Korea,61452","institution_ids":["https://openalex.org/I152238500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5077748142"],"corresponding_institution_ids":["https://openalex.org/I152238500"],"apc_list":null,"apc_paid":null,"fwci":0.123,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36240605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"9","issue":null,"first_page":"221","last_page":"226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9983000159263611,"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":0.9983000159263611,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980000257492065,"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/T10862","display_name":"AI in cancer detection","score":0.9966999888420105,"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/lossy-compression","display_name":"Lossy compression","score":0.867445707321167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7637920379638672},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.75226891040802},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7224648594856262},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6626676917076111},{"id":"https://openalex.org/keywords/uncompressed-video","display_name":"Uncompressed video","score":0.6406316757202148},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44987407326698303},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.4334549307823181},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43044567108154297},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.41663044691085815},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.39537033438682556},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3728044033050537},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.25477921962738037}],"concepts":[{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.867445707321167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7637920379638672},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.75226891040802},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7224648594856262},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6626676917076111},{"id":"https://openalex.org/C162478608","wikidata":"https://www.wikidata.org/wiki/Q4011369","display_name":"Uncompressed video","level":4,"score":0.6406316757202148},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44987407326698303},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.4334549307823181},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43044567108154297},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.41663044691085815},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.39537033438682556},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3728044033050537},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25477921962738037},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic57133.2023.10067012","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icaiic57133.2023.10067012","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8943200322","display_name":null,"funder_award_id":"NRF-2018R1D1A1B07048338","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2140196014","https://openalex.org/W2187089797","https://openalex.org/W2890521849","https://openalex.org/W2954996726","https://openalex.org/W2963980515","https://openalex.org/W3012110212","https://openalex.org/W3034922525","https://openalex.org/W3118608800","https://openalex.org/W3145444543","https://openalex.org/W3192455587","https://openalex.org/W3210165700","https://openalex.org/W3211209457","https://openalex.org/W4210751763","https://openalex.org/W4290974101","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W3210332869","https://openalex.org/W2032334472","https://openalex.org/W4327499886","https://openalex.org/W4294006383","https://openalex.org/W2096442341","https://openalex.org/W3202870363","https://openalex.org/W2115343698","https://openalex.org/W2380116549","https://openalex.org/W2386792074","https://openalex.org/W2056051076"],"abstract_inverted_index":{"Lossy":[0],"image":[1,16],"compression":[2,28],"provides":[3],"an":[4],"efficient":[5],"solution":[6],"to":[7,31,65,113,133,180],"the":[8,42,55,67,71,92,97,106,123,135,138,163,174],"exchange":[9],"and":[10],"storage":[11],"of":[12,15,25,57,70,117,137,146],"large":[13],"volumes":[14],"data":[17,80],"for":[18,104,115],"various":[19],"applications.":[20],"The":[21,86,155],"main":[22],"design":[23],"principle":[24],"a":[26,47,58,78,143],"lossy":[27],"algorithm":[29],"is":[30,109,126],"discard":[32],"visually":[33],"insignificant":[34],"information":[35],"as":[36,38],"much":[37],"possible":[39],"while":[40,173],"keeping":[41],"resulted":[43],"visible":[44],"artifacts":[45],"at":[46],"minimum.":[48],"However,":[49],"these":[50],"unperceivable":[51],"defects":[52],"significantly":[53],"degrade":[54],"performance":[56,69,98,125],"trained":[59],"deep":[60],"learning":[61],"(DL)":[62],"model.":[63],"Therefore,":[64],"improve":[66],"classification":[68,116,148],"models":[72],"on":[73,100,158,169,182],"noisy":[74],"images,":[75,122],"we":[76,141],"propose":[77],"noise-based":[79],"augmentation":[81,165],"technique":[82],"called":[83],"noise-cuts-noise":[84],"approach.":[85],"simulation":[87],"analysis":[88],"have":[89],"shown":[90],"that":[91],"proposed":[93,139,164],"method":[94,166],"efficiently":[95],"mitigates":[96],"gap":[99],"highly":[101,159],"compressed":[102,160],"images":[103,161,172],"example,":[105],"accuracy":[107,157,175],"difference":[108,176],"reduced":[110,177],"from":[111,178],"11%":[112],"2%":[114],"natural":[118],"images.":[119,154,185],"For":[120],"uncompressed":[121],"model":[124,156],"either":[127],"preserved":[128],"or":[129],"improved.":[130],"In":[131],"addition,":[132],"validate":[134],"usefulness":[136],"method,":[140],"considered":[142],"case":[144],"study":[145],"multi-label":[147],"task":[149],"in":[150],"chest":[151],"X-ray":[152],"(CXR)":[153],"with":[162],"increased":[167],"2<sup>%</sup>":[168],"higher":[170],"resolution":[171,184],"6%":[179],"1%":[181],"smaller":[183]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}