{"id":"https://openalex.org/W4403791423","doi":"https://doi.org/10.1145/3664647.3680937","title":"MLP Embedded Inverse Tone Mapping","display_name":"MLP Embedded Inverse Tone Mapping","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791423","doi":"https://doi.org/10.1145/3664647.3680937"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680937","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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/A5107592174","display_name":"Panjun Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Panjun Liu","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0009-0008-2273-6509","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017417267","display_name":"Jiacheng Li","orcid":"https://orcid.org/0000-0002-4215-6754"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiacheng Li","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-4215-6754","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019918709","display_name":"Lizhi Wang","orcid":"https://orcid.org/0000-0002-1953-3339"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhi Wang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1953-3339","affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003217535","display_name":"Zheng-Jun Zha","orcid":"https://orcid.org/0000-0003-2510-8993"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng-Jun Zha","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0003-2510-8993","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008612863","display_name":"Zhiwei Xiong","orcid":"https://orcid.org/0000-0002-9787-7460"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwei Xiong","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-9787-7460","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1283","last_page":"1291"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9983999729156494,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9983999729156494,"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":0.9979000091552734,"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/T11666","display_name":"Color Science and Applications","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tone-mapping","display_name":"Tone mapping","score":0.7896609306335449},{"id":"https://openalex.org/keywords/tone","display_name":"Tone (literature)","score":0.692488431930542},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6790972352027893},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.4208492338657379},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3377707600593567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32090944051742554},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2696438431739807},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15637433528900146}],"concepts":[{"id":"https://openalex.org/C8641274","wikidata":"https://www.wikidata.org/wiki/Q1030958","display_name":"Tone mapping","level":4,"score":0.7896609306335449},{"id":"https://openalex.org/C2780583480","wikidata":"https://www.wikidata.org/wiki/Q1366327","display_name":"Tone (literature)","level":2,"score":0.692488431930542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6790972352027893},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.4208492338657379},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3377707600593567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32090944051742554},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2696438431739807},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15637433528900146},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680937","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W22090881","https://openalex.org/W1580389772","https://openalex.org/W2008675431","https://openalex.org/W2015196405","https://openalex.org/W2081404530","https://openalex.org/W2127129827","https://openalex.org/W2133665775","https://openalex.org/W2141983208","https://openalex.org/W2469102383","https://openalex.org/W2566124125","https://openalex.org/W2792319557","https://openalex.org/W2889652284","https://openalex.org/W2963516811","https://openalex.org/W2983118621","https://openalex.org/W2998556496","https://openalex.org/W3000044499","https://openalex.org/W3009213340","https://openalex.org/W3034617042","https://openalex.org/W3036045183","https://openalex.org/W3176234575","https://openalex.org/W3184674254","https://openalex.org/W3209795834","https://openalex.org/W4211244138","https://openalex.org/W4214762230","https://openalex.org/W4304087051","https://openalex.org/W4312791910","https://openalex.org/W4317987932","https://openalex.org/W4386059972","https://openalex.org/W4386071607","https://openalex.org/W4386075518","https://openalex.org/W4386076237","https://openalex.org/W4386432290","https://openalex.org/W4387968013","https://openalex.org/W4388193691"],"related_works":["https://openalex.org/W2342978393","https://openalex.org/W2998409557","https://openalex.org/W1981480020","https://openalex.org/W2401918037","https://openalex.org/W2020785490","https://openalex.org/W1967499694","https://openalex.org/W1987078514","https://openalex.org/W2966189336","https://openalex.org/W2144227365","https://openalex.org/W3136079841"],"abstract_inverted_index":{"The":[0,214],"advent":[1],"of":[2,30,63,137,179],"High":[3],"Dynamic":[4,54],"Range/Wide":[5],"Color":[6],"Gamut":[7],"(HDR/WCG)":[8],"display":[9],"technology":[10],"has":[11],"made":[12],"significant":[13,41],"progress":[14],"in":[15,126,207],"providing":[16],"exceptional":[17],"richness":[18],"and":[19,190,211],"vibrancy":[20],"for":[21,58,83,96,132],"the":[22,27,61,133,155,164,173,177,180,192],"human":[23],"visual":[24],"experience.":[25],"However,":[26],"widespread":[28],"adoption":[29],"HDR/WCG":[31,47,85,119,139,184],"images":[32,48],"is":[33,170],"hindered":[34],"by":[35,88],"their":[36,71,93],"substantial":[37],"storage":[38],"requirements,":[39],"imposing":[40],"bandwidth":[42],"challenges":[43],"during":[44],"distribution.":[45],"Besides,":[46],"are":[49,216],"often":[50],"tone-mapped":[51],"into":[52,92],"Standard":[53],"Range":[55],"(SDR)":[56],"versions":[57],"compatibility,":[59],"necessitating":[60],"usage":[62],"inverse":[64],"Tone":[65],"Mapping":[66],"(iTM)":[67],"techniques":[68],"to":[69,106,118,148,153,158],"reconstruct":[70],"original":[72,181],"representation.":[73],"In":[74],"this":[75],"work,":[76],"we":[77,101,141],"propose":[78],"a":[79,103,108,127,143],"meta-transfer":[80],"learning":[81,135],"framework":[82,194,203],"practical":[84],"media":[86],"transmission":[87],"embedding":[89],"image-wise":[90,160],"metadata":[91],"SDR":[94,116,174],"counterparts":[95],"later":[97],"iTM":[98,129,161],"reconstruction.":[99],"Specifically,":[100],"devise":[102],"meta-learning":[104],"strategy":[105],"pre-train":[107],"lightweight":[109],"multilayer":[110],"perceptron":[111],"(MLP)":[112],"model":[113,157],"that":[114],"maps":[115],"pixels":[117],"ones":[120],"on":[121,183],"an":[122,159],"external":[123],"dataset,":[124],"resulting":[125],"domain-wise":[128],"model.":[130,162],"Subsequently,":[131],"transfer":[134],"process":[136],"each":[138],"image,":[140,175],"present":[142],"spatial-aware":[144],"online":[145],"mining":[146],"mechanism":[147],"select":[149],"challenging":[150],"training":[151],"pairs":[152],"adapt":[154],"meta-trained":[156],"Finally,":[163],"adapted":[165],"MLP,":[166],"embedded":[167],"as":[168],"metadata,":[169],"transmitted":[171],"alongside":[172],"facilitating":[176],"reconstruction":[178],"image":[182],"displays.":[185],"We":[186],"conduct":[187],"extensive":[188],"experiments":[189],"evaluate":[191],"proposed":[193],"with":[195,199],"diverse":[196],"metrics.":[197],"Compared":[198],"existing":[200],"solutions,":[201],"our":[202],"shows":[204],"superior":[205],"performance":[206],"fidelity,":[208],"minimal":[209],"latency,":[210],"negligible":[212],"overhead.":[213],"codes":[215],"available":[217],"at":[218],"https://github.com/pjliu3/MLP_iTM.":[219]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
