{"id":"https://openalex.org/W115742922","doi":"https://doi.org/10.5555/2627435.2670317","title":"The student-t mixture as a natural image patch prior with application to image compression","display_name":"The student-t mixture as a natural image patch prior with application to image compression","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W115742922","doi":"https://doi.org/10.5555/2627435.2670317","mag":"115742922"},"language":"en","primary_location":{"id":"pmh:oai:archive.ugent.be:5706323","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-5706323","pdf_url":"http://www.jmlr.org/papers/volume15/vandenoord14a/vandenoord14a.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN: 1533-7928","raw_type":"info:eu-repo/semantics/article"},"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://www.jmlr.org/papers/volume15/vandenoord14a/vandenoord14a.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039776447","display_name":"A\u00e4ron van den Oord","orcid":null},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"A\u00e4ron van den Oord","raw_affiliation_strings":["Department of Electronics and Information Systems Ghent University  Ghent Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Information Systems Ghent University  Ghent Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066537128","display_name":"Benjamin Schrauwen","orcid":null},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Benjamin Schrauwen","raw_affiliation_strings":["Department of Electronics and Information Systems Ghent University  Ghent Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Information Systems Ghent University  Ghent Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32597200"],"apc_list":null,"apc_paid":null,"fwci":2.4261,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.90826429,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"15","issue":"1","first_page":"2061","last_page":"2086"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.998199999332428,"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":0.998199999332428,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9980000257492065,"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"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9933000206947327,"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/lossy-compression","display_name":"Lossy compression","score":0.7200669050216675},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6858288049697876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6580122113227844},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.6538529396057129},{"id":"https://openalex.org/keywords/deblurring","display_name":"Deblurring","score":0.6478978395462036},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6448454260826111},{"id":"https://openalex.org/keywords/inpainting","display_name":"Inpainting","score":0.6192711591720581},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.559943675994873},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.529705286026001},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48715803027153015},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46071892976760864},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.40245336294174194},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3081139922142029},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.2902027368545532}],"concepts":[{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.7200669050216675},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6858288049697876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6580122113227844},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.6538529396057129},{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.6478978395462036},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6448454260826111},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.6192711591720581},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.559943675994873},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.529705286026001},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48715803027153015},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46071892976760864},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.40245336294174194},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3081139922142029},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.2902027368545532}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:archive.ugent.be:5706323","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-5706323","pdf_url":"http://www.jmlr.org/papers/volume15/vandenoord14a/vandenoord14a.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN: 1533-7928","raw_type":"info:eu-repo/semantics/article"},{"id":"mag:115742922","is_oa":false,"landing_page_url":"https://jmlr.org/papers/volume15/vandenoord14a/vandenoord14a.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":{"id":"pmh:oai:archive.ugent.be:5706323","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-5706323","pdf_url":"http://www.jmlr.org/papers/volume15/vandenoord14a/vandenoord14a.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN: 1533-7928","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W115742922.pdf","grobid_xml":"https://content.openalex.org/works/W115742922.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W85271845","https://openalex.org/W1579925870","https://openalex.org/W1584151564","https://openalex.org/W1972959470","https://openalex.org/W1977316355","https://openalex.org/W2040903332","https://openalex.org/W2049633694","https://openalex.org/W2069959554","https://openalex.org/W2072128103","https://openalex.org/W2072514525","https://openalex.org/W2078950386","https://openalex.org/W2079256176","https://openalex.org/W2096933452","https://openalex.org/W2097713019","https://openalex.org/W2099563019","https://openalex.org/W2101047103","https://openalex.org/W2105464873","https://openalex.org/W2109034718","https://openalex.org/W2109320267","https://openalex.org/W2116064496","https://openalex.org/W2121927366","https://openalex.org/W2131686571","https://openalex.org/W2136762275","https://openalex.org/W2137971377","https://openalex.org/W2138448681","https://openalex.org/W2141089030","https://openalex.org/W2152681473","https://openalex.org/W2153663612","https://openalex.org/W2166698530","https://openalex.org/W2166816543","https://openalex.org/W2170111110","https://openalex.org/W2172275395","https://openalex.org/W2294277889","https://openalex.org/W2536599074","https://openalex.org/W2606321545","https://openalex.org/W2953106669"],"related_works":["https://openalex.org/W2172275395","https://openalex.org/W2135181320","https://openalex.org/W2148464528","https://openalex.org/W2097268041","https://openalex.org/W2170111110","https://openalex.org/W2133665775","https://openalex.org/W2121927366","https://openalex.org/W3168321850","https://openalex.org/W2758801564","https://openalex.org/W2194775991","https://openalex.org/W2152595235","https://openalex.org/W2144414973","https://openalex.org/W2118522007","https://openalex.org/W2117853077","https://openalex.org/W2109034718","https://openalex.org/W2099471712","https://openalex.org/W2094952625","https://openalex.org/W2083380015","https://openalex.org/W2024476015","https://openalex.org/W1753482797"],"abstract_inverted_index":{"Recent":[0],"results":[1,170],"have":[2],"shown":[3],"that":[4,105,149,163],"Gaussian":[5],"mixture":[6,93,100],"models":[7],"(GMMs)":[8],"are":[9,32],"remarkably":[10],"good":[11],"at":[12],"density":[13],"modeling":[14],"of":[15,25,80,89,112,121,138],"natural":[16],"image":[17,56,61,96,125,142],"patches,":[18],"especially":[19],"given":[20],"their":[21],"simplicity.":[22],"In":[23],"terms":[24,111],"log":[26,113],"likelihood":[27],"on":[28,69,83],"real-valued":[29],"data":[30],"they":[31,67],"comparable":[33,171],"with":[34,118,169],"the":[35,87,98,119,122,131,136,164],"best":[36],"performing":[37],"techniques":[38,166],"published,":[39],"easily":[40,151],"outperforming":[41],"more":[42],"advanced":[43],"ones,":[44],"such":[45,59,74],"as":[46,60,75],"deep":[47],"belief":[48],"networks.":[49],"They":[50],"can":[51,150],"be":[52,152],"applied":[53],"to":[54,135,154,172],"various":[55],"processing":[57],"tasks,":[58],"denoising,":[62],"deblurring":[63],"and":[64,78,133,140,144],"inpainting,":[65],"where":[66],"improve":[68],"other":[70,155],"generic":[71],"prior":[72],"methods,":[73],"sparse":[76],"coding":[77,147],"field":[79],"experts.":[81],"Based":[82],"this":[84],"we":[85,161],"propose":[86,145],"use":[88],"another,":[90],"even":[91],"richer":[92],"model":[94,101],"based":[95],"prior:":[97],"Student-t":[99],"(STM).":[102],"We":[103,128],"demonstrate":[104],"it":[106],"convincingly":[107],"surpasses":[108],"GMMs":[109],"in":[110,124],"likelihood,":[114],"achieving":[115],"performance":[116],"competitive":[117],"state":[120],"art":[123],"patch":[126],"modeling.":[127],"apply":[129],"both":[130],"GMM":[132],"STM":[134],"task":[137],"lossy":[139],"lossless":[141],"compression,":[143],"efficient":[146],"schemes":[148],"extended":[153],"unsupervised":[156],"machine":[157],"learning":[158],"models.":[159],"Finally,":[160],"show":[162],"suggested":[165],"outperform":[167],"JPEG,":[168],"or":[173],"better":[174],"than":[175],"JPEG":[176],"2000.":[177]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2025-10-10T17:16:08.811792","created_date":"2025-10-10T00:00:00"}