{"id":"https://openalex.org/W4386596922","doi":"https://doi.org/10.1109/icip49359.2023.10222630","title":"Pre-Training with Fractal Images Facilitates Learned Image Quality Estimation","display_name":"Pre-Training with Fractal Images Facilitates Learned Image Quality Estimation","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386596922","doi":"https://doi.org/10.1109/icip49359.2023.10222630"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222630","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5114081272","display_name":"M. J. Silbernagel","orcid":null},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Malte Silbernagel","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034084717","display_name":"Thomas Wiegand","orcid":"https://orcid.org/0000-0002-1121-2581"},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Wiegand","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009614926","display_name":"Peter Eisert","orcid":"https://orcid.org/0000-0001-8378-4805"},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Peter Eisert","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020506776","display_name":"Sebastian Bosse","orcid":"https://orcid.org/0000-0002-8104-3904"},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian Bosse","raw_affiliation_strings":["Fraunhofer HHI,Berlin,Germany","Fraunhofer HHI, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer HHI,Berlin,Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Fraunhofer HHI, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800274787"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"2625","last_page":"2629"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","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/T11165","display_name":"Image and Video Quality Assessment","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":0.9876999855041504,"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":0.9768000245094299,"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/computer-science","display_name":"Computer science","score":0.8168274164199829},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7915472388267517},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6521146297454834},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6484647989273071},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6146632432937622},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5591485500335693},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5419328212738037},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5414704084396362},{"id":"https://openalex.org/keywords/fractal","display_name":"Fractal","score":0.5037402510643005},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49324363470077515},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.48525309562683105},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.482255756855011},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4773257374763489},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4767804443836212},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4356166124343872},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3661489486694336},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3294762074947357},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07757437229156494}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8168274164199829},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7915472388267517},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6521146297454834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6484647989273071},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6146632432937622},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5591485500335693},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5419328212738037},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5414704084396362},{"id":"https://openalex.org/C40636538","wikidata":"https://www.wikidata.org/wiki/Q81392","display_name":"Fractal","level":2,"score":0.5037402510643005},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49324363470077515},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.48525309562683105},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.482255756855011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4773257374763489},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4767804443836212},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4356166124343872},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3661489486694336},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3294762074947357},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07757437229156494},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip49359.2023.10222630","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip49359.2023.10222630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/508836","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/508836","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1987489060","https://openalex.org/W1992869494","https://openalex.org/W2029629075","https://openalex.org/W2108598243","https://openalex.org/W2133665775","https://openalex.org/W2141983208","https://openalex.org/W2158564760","https://openalex.org/W2159269332","https://openalex.org/W2161907179","https://openalex.org/W2168023013","https://openalex.org/W2563786098","https://openalex.org/W2905530235","https://openalex.org/W2963975576","https://openalex.org/W2964631455","https://openalex.org/W3095351420","https://openalex.org/W3112788634","https://openalex.org/W3119746452","https://openalex.org/W3134970617","https://openalex.org/W3194280054","https://openalex.org/W3194297316","https://openalex.org/W3210514413","https://openalex.org/W4212848528","https://openalex.org/W4320350303","https://openalex.org/W6637373629","https://openalex.org/W6647225686","https://openalex.org/W6684791271","https://openalex.org/W6787774617","https://openalex.org/W6849397646"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"Today\u2019s":[0],"image":[1],"quality":[2,25,41,77],"estimation":[3,42],"is":[4,17],"widely":[5],"dominated":[6],"by":[7],"learning-based":[8,40],"approaches.":[9],"The":[10],"availability":[11],"of":[12],"annotated,":[13],"i.e.":[14],"rated,":[15],"images":[16,56],"often":[18],"a":[19,35,73],"bottleneck":[20],"in":[21,90],"training":[22,83],"data-driven":[23],"visual":[24],"models":[26],"and":[27,61,79,94],"hinders":[28],"their":[29],"generalization":[30],"power.":[31],"This":[32],"paper":[33],"proposed":[34],"novel":[36],"pre-training":[37,70],"scheme":[38],"for":[39],"that":[43,81],"does":[44],"not":[45],"rely":[46],"on":[47,72],"human-annotated":[48],"datasets,":[49],"but":[50],"leverages":[51],"synthetic":[52],"fractal":[53],"images.":[54],"These":[55],"can":[57,85],"be":[58,86],"synthesized":[59],"inexhaustibly":[60],"are":[62],"inherently":[63],"labeled":[64],"during":[65],"generation.":[66],"We":[67],"evaluate":[68],"the":[69,82],"strategy":[71],"popular":[74],"neural":[75],"network-based":[76],"model":[78],"show":[80],"effort":[84],"reduced":[87],"significantly,":[88],"resulting":[89],"better":[91],"final":[92],"accuracy":[93],"faster":[95],"convergence":[96],"speed.":[97]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
