{"id":"https://openalex.org/W2939278768","doi":"https://doi.org/10.1109/icassp.2019.8682374","title":"An Image Coding Approach Based on Mixture-of-experts Regression Using Epanechnikov Kernel","display_name":"An Image Coding Approach Based on Mixture-of-experts Regression Using Epanechnikov Kernel","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2939278768","doi":"https://doi.org/10.1109/icassp.2019.8682374","mag":"2939278768"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5064200420","display_name":"Boning Liu","orcid":"https://orcid.org/0000-0002-3014-6049"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Boning Liu","raw_affiliation_strings":["College of Communication Engineering, Jilin University, Changchun, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Communication Engineering, Jilin University, Changchun, P. R. China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053490763","display_name":"Yan Zhao","orcid":"https://orcid.org/0000-0002-5319-0202"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhao","raw_affiliation_strings":["College of Communication Engineering, Jilin University, Changchun, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Communication Engineering, Jilin University, Changchun, P. R. China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022114091","display_name":"Xiaomeng Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaomeng Jiang","raw_affiliation_strings":["College of Mathematics, Jilin University, Changchun, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics, Jilin University, Changchun, P. R. China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101855294","display_name":"Shigang Wang","orcid":"https://orcid.org/0000-0002-3598-9352"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shigang Wang","raw_affiliation_strings":["College of Communication Engineering, Jilin University, Changchun, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Communication Engineering, Jilin University, Changchun, P. R. China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5064200420"],"corresponding_institution_ids":["https://openalex.org/I194450716"],"apc_list":null,"apc_paid":null,"fwci":0.3037,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.58905838,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1807","last_page":"1811"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9972000122070312,"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":0.9972000122070312,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9965000152587891,"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/T10057","display_name":"Face and Expression Recognition","score":0.9897000193595886,"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/kernel-regression","display_name":"Kernel regression","score":0.663820743560791},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6278594136238098},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5397660732269287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5161024928092957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5106003880500793},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.49506035447120667},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.48002520203590393},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.47443902492523193},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46474000811576843},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3694390654563904},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.36552923917770386},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2561737895011902},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19170346856117249},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.08831062912940979}],"concepts":[{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.663820743560791},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6278594136238098},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5397660732269287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5161024928092957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5106003880500793},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.49506035447120667},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.48002520203590393},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.47443902492523193},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46474000811576843},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3694390654563904},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.36552923917770386},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2561737895011902},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19170346856117249},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.08831062912940979},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":15,"referenced_works":["https://openalex.org/W1491079761","https://openalex.org/W1968367053","https://openalex.org/W1985690171","https://openalex.org/W1998586455","https://openalex.org/W2006262236","https://openalex.org/W2050942942","https://openalex.org/W2050947749","https://openalex.org/W2073306061","https://openalex.org/W2103280265","https://openalex.org/W2110806509","https://openalex.org/W2125796864","https://openalex.org/W2129222000","https://openalex.org/W2579432226","https://openalex.org/W2735033053","https://openalex.org/W6668920329"],"related_works":["https://openalex.org/W2213164457","https://openalex.org/W122742822","https://openalex.org/W2115519811","https://openalex.org/W2157272197","https://openalex.org/W4213162954","https://openalex.org/W1594974787","https://openalex.org/W4241010850","https://openalex.org/W2766091922","https://openalex.org/W1507211460","https://openalex.org/W2246292763"],"abstract_inverted_index":{"In":[0,31,71],"this":[1],"paper,":[2],"we":[3,54],"propose":[4],"an":[5,44],"optimal":[6,61],"modeling":[7],"framework":[8],"for":[9,59,94],"image":[10,96],"compression":[11],"using":[12],"EMM":[13,45],"(Epanechnikov":[14],"Mixture":[15,28,107],"Model).":[16],"Epanechnikov":[17,27],"Kernel":[18],"and":[19,52],"its":[20],"correlated":[21],"statistics":[22],"are":[23,41,65],"basement":[24],"of":[25,37,74],"our":[26,32],"Regression":[29,108],"(EMR).":[30],"scheme,":[33],"the":[34,38,60,72,76,80,88,95],"stochastic":[35],"processes":[36],"pixel":[39],"values":[40],"modelled":[42],"as":[43],"with":[46,97],"K":[47],"experts":[48],"in":[49],"three-dimensional":[50],"space":[51],"then":[53],"use":[55],"EMR":[56],"to":[57,105],"search":[58],"solution,":[62],"whose":[63],"parameters":[64],"determined":[66],"through":[67],"EM":[68],"(Expectation-Maximization)":[69],"algorithm.":[70],"process":[73],"regression,":[75],"conditional":[77],"density":[78],"is":[79,91],"regression":[81],"kernel":[82],"function.":[83],"Experimental":[84],"results":[85],"show":[86],"that":[87],"proposed":[89],"scheme":[90],"effective":[92],"especially":[93],"complex":[98],"texture":[99],"without":[100],"consuming":[101],"extra":[102],"bits":[103],"compared":[104],"Gaussian":[106],"(GMR).":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}