{"id":"https://openalex.org/W4317555354","doi":"https://doi.org/10.1109/pcs56426.2022.10017994","title":"Flexible luma-chroma bit allocation in learned image compression for high-fidelity sharper images","display_name":"Flexible luma-chroma bit allocation in learned image compression for high-fidelity sharper images","publication_year":2022,"publication_date":"2022-12-07","ids":{"openalex":"https://openalex.org/W4317555354","doi":"https://doi.org/10.1109/pcs56426.2022.10017994"},"language":"en","primary_location":{"id":"doi:10.1109/pcs56426.2022.10017994","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/pcs56426.2022.10017994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Picture Coding Symposium (PCS)","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/A5050551366","display_name":"O. Ugur Ulas","orcid":null},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"O. Ugur Ulas","raw_affiliation_strings":["Koç University,Department of Electrical and Electronics Engineering,Istanbul,Turkey,34450"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Koç University,Department of Electrical and Electronics Engineering,Istanbul,Turkey,34450","institution_ids":["https://openalex.org/I1351752"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034648478","display_name":"A. Murat Tekalp","orcid":"https://orcid.org/0000-0003-1465-8121"},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"A. Murat Tekalp","raw_affiliation_strings":["Koç University,Department of Electrical and Electronics Engineering,Istanbul,Turkey,34450"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Koç University,Department of Electrical and Electronics Engineering,Istanbul,Turkey,34450","institution_ids":["https://openalex.org/I1351752"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1351752"],"apc_list":null,"apc_paid":null,"fwci":0.2031,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.50510223,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"31","issue":null,"first_page":"31","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998999834060669,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998999834060669,"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":0.9990000128746033,"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":0.9987999796867371,"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":0.700946569442749},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6620671153068542},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.549981951713562},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5334696173667908},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.49699142575263977},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.45485571026802063},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4386088252067566},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41156718134880066},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3226754069328308},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.25920072197914124}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.700946569442749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6620671153068542},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.549981951713562},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5334696173667908},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.49699142575263977},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.45485571026802063},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4386088252067566},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41156718134880066},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3226754069328308},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25920072197914124},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pcs56426.2022.10017994","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/pcs56426.2022.10017994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Picture Coding Symposium (PCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2046119925","https://openalex.org/W2117804578","https://openalex.org/W2146395539","https://openalex.org/W2552465432","https://openalex.org/W2769654144","https://openalex.org/W2785562966","https://openalex.org/W2949361041","https://openalex.org/W3034469748","https://openalex.org/W3097448661","https://openalex.org/W3102015846","https://openalex.org/W3133927215","https://openalex.org/W3179468628","https://openalex.org/W3189429630","https://openalex.org/W3201359752","https://openalex.org/W3207406391","https://openalex.org/W4230291482","https://openalex.org/W4287605633","https://openalex.org/W6631190155","https://openalex.org/W6754634825"],"related_works":["https://openalex.org/W2381850946","https://openalex.org/W4380449851","https://openalex.org/W3125091513","https://openalex.org/W4318832338","https://openalex.org/W4248383205","https://openalex.org/W1919390113","https://openalex.org/W4234745530","https://openalex.org/W2146383839","https://openalex.org/W4390516098","https://openalex.org/W2231829109"],"abstract_inverted_index":{"High-fidelity":[0],"learned":[1,76],"image/video":[2],"compression":[3,155],"solutions":[4],"are":[5],"typically":[6,39],"optimized":[7],"with":[8,130,163,178],"respect":[9],"to":[10,67,93,102,114,186],"l1":[11],"or":[12],"l2":[13],"loss":[14,47,56],"in":[15,34,55,84],"RGB":[16,22,135,190],"444":[17],"format":[18],"and":[19,166],"evaluated":[20],"by":[21,41,172],"PSNR.":[23,136],"It":[24],"is":[25,38],"well-known":[26],"that":[27,126,139,160],"optimization":[28],"of":[29,57,109],"a":[30,43,64],"fidelity":[31,73],"criterion":[32],"results":[33,158],"blurry":[35],"images,":[36],"which":[37],"alleviated":[40],"adding":[42],"content-based":[44],"and/or":[45],"adversarial":[46],"terms.":[48],"However,":[49],"such":[50],"conditional":[51],"generative":[52],"models":[53,174,188],"result":[54],"fidelity.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62],"propose":[63],"simple":[65],"solution":[66],"obtain":[68,115],"sharper":[69,116,161],"images":[70,117,162],"without":[71,118],"losing":[72],"based":[74,122],"on":[75,123],"flexible-rate":[77],"coding":[78],"using":[79],"gained":[80],"variational":[81],"auto-encoder":[82],"(gained-VAE)":[83],"the":[85,107,124,140,179,193],"luma-chroma":[86,96,144,182],"(YCrCb":[87],"444)":[88],"domain.":[89],"This":[90],"allows":[91],"us":[92],"implement":[94],"image-adaptive":[95,143,181],"bit":[97,145],"allocation":[98,146,184],"during":[99],"inference,":[100],"i.e.,":[101],"increase":[103],"Y":[104,127,167],"PSNR":[105,113,128,168],"at":[106,192],"expense":[108],"slightly":[110],"lower":[111],"chroma":[112],"introducing":[119],"color":[120],"artifacts":[121],"observation":[125],"correlates":[129],"image":[131,154],"sharpness":[132],"better":[133,164],"than":[134],"We":[137],"note":[138],"proposed":[141,180],"inference-time":[142],"strategy":[147],"can":[148,169],"be":[149,170],"incorporated":[150],"into":[151],"any":[152],"VAE-based":[153],"model.":[156],"Experimental":[157],"show":[159],"VMAF":[165],"obtained":[171],"optimizing":[173,189],"for":[175],"YCrCb":[176],"MSE":[177,191],"bit/quality":[183],"compared":[185],"state-of-the-art":[187],"same":[194],"bpp.":[195]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}