{"id":"https://openalex.org/W4317555242","doi":"https://doi.org/10.1109/pcs56426.2022.10018003","title":"Enhancement of CNN-based Probability Modeling by Locally Trained Adaptive Prediction for Efficient Lossless Image Coding","display_name":"Enhancement of CNN-based Probability Modeling by Locally Trained Adaptive Prediction for Efficient Lossless Image Coding","publication_year":2022,"publication_date":"2022-12-07","ids":{"openalex":"https://openalex.org/W4317555242","doi":"https://doi.org/10.1109/pcs56426.2022.10018003"},"language":"en","primary_location":{"id":"doi:10.1109/pcs56426.2022.10018003","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/pcs56426.2022.10018003","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/A5102456088","display_name":"Keisuke Kaji","orcid":null},"institutions":[{"id":"https://openalex.org/I161296585","display_name":"Tokyo University of Science","ror":"https://ror.org/05sj3n476","country_code":"JP","type":"education","lineage":["https://openalex.org/I161296585"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keisuke Kaji","raw_affiliation_strings":["Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510","institution_ids":["https://openalex.org/I161296585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049659355","display_name":"Yasuyo Kita","orcid":"https://orcid.org/0000-0001-8139-7406"},"institutions":[{"id":"https://openalex.org/I161296585","display_name":"Tokyo University of Science","ror":"https://ror.org/05sj3n476","country_code":"JP","type":"education","lineage":["https://openalex.org/I161296585"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasuyo Kita","raw_affiliation_strings":["Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510","institution_ids":["https://openalex.org/I161296585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102924720","display_name":"Ichiro Matsuda","orcid":"https://orcid.org/0009-0000-4556-975X"},"institutions":[{"id":"https://openalex.org/I161296585","display_name":"Tokyo University of Science","ror":"https://ror.org/05sj3n476","country_code":"JP","type":"education","lineage":["https://openalex.org/I161296585"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ichiro Matsuda","raw_affiliation_strings":["Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510","institution_ids":["https://openalex.org/I161296585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102314788","display_name":"Susumu Itoh","orcid":null},"institutions":[{"id":"https://openalex.org/I161296585","display_name":"Tokyo University of Science","ror":"https://ror.org/05sj3n476","country_code":"JP","type":"education","lineage":["https://openalex.org/I161296585"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Susumu Itoh","raw_affiliation_strings":["Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo University of Science 2641 Yamazaki, Noda-shi,Faculty of Science and Technology,Chiba,JAPAN,278-8510","institution_ids":["https://openalex.org/I161296585"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056040328","display_name":"Yusuke Kameda","orcid":"https://orcid.org/0000-0001-8503-4098"},"institutions":[{"id":"https://openalex.org/I42999171","display_name":"Sophia University","ror":"https://ror.org/01nckkm68","country_code":"JP","type":"education","lineage":["https://openalex.org/I42999171"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yusuke Kameda","raw_affiliation_strings":["Sophia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sophia University","institution_ids":["https://openalex.org/I42999171"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14493229,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"79","last_page":"83"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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":0.9994999766349792,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9988999962806702,"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":0.9987000226974487,"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/autoregressive-model","display_name":"Autoregressive model","score":0.7288635969161987},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6741765737533569},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.6691800951957703},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6571225523948669},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6038715839385986},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5535222291946411},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5345489382743835},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5032681822776794},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4649782180786133},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4460684061050415},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40986329317092896},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4009955823421478},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.2758469581604004},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23631015419960022},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12373200058937073}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7288635969161987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6741765737533569},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.6691800951957703},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6571225523948669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6038715839385986},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5535222291946411},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5345489382743835},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5032681822776794},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4649782180786133},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4460684061050415},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40986329317092896},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4009955823421478},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2758469581604004},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23631015419960022},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12373200058937073}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pcs56426.2022.10018003","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/pcs56426.2022.10018003","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1524627920","https://openalex.org/W2295929469","https://openalex.org/W2423557781","https://openalex.org/W2518435514","https://openalex.org/W2903167847","https://openalex.org/W2989439739","https://openalex.org/W3081397486","https://openalex.org/W3138372559","https://openalex.org/W4249276014","https://openalex.org/W6640963894","https://openalex.org/W6685352114","https://openalex.org/W6732492507","https://openalex.org/W6736734616"],"related_works":["https://openalex.org/W3093612317","https://openalex.org/W2613736958","https://openalex.org/W2406522397","https://openalex.org/W2175746458","https://openalex.org/W2732542196","https://openalex.org/W2760085659","https://openalex.org/W4205367172","https://openalex.org/W4239686595","https://openalex.org/W2170329003","https://openalex.org/W2738221750"],"abstract_inverted_index":{"An":[0],"autoregressive":[1],"image":[2,12,21,63],"generative":[3,27],"model":[4,28,85],"that":[5,52,73],"estimates":[6],"the":[7,54,66,74,78,83,89],"conditional":[8],"probability":[9,56],"distributions":[10],"of":[11,50,88],"signals":[13],"pel-by-pel":[14],"is":[15],"a":[16,26,31,39],"promising":[17],"tool":[18],"for":[19,61,86],"lossless":[20],"coding.":[22],"In":[23],"this":[24],"paper,":[25],"based":[29],"on":[30],"convolutional":[32],"neural":[33],"network":[34],"(CNN)":[35],"was":[36],"combined":[37],"with":[38],"locally":[40],"trained":[41],"adaptive":[42],"predictor":[43],"to":[44,64],"improve":[45],"its":[46],"accuracy.":[47],"Furthermore,":[48],"sets":[49],"parameters":[51],"adjust":[53],"estimated":[55],"distribution":[57],"were":[58],"numerically":[59],"optimized":[60],"each":[62],"minimize":[65],"resulting":[67],"coding":[68,79],"rate.":[69],"Simulation":[70],"results":[71],"indicate":[72],"proposed":[75],"method":[76],"improves":[77],"efficiency":[80],"obtained":[81],"by":[82],"CNN-based":[84],"most":[87],"tested":[90],"images.":[91]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
