{"id":"https://openalex.org/W3089737543","doi":"https://doi.org/10.1109/icip40778.2020.9190969","title":"Augmenting JPEG2000 With Wavelet Coefficient Prediction","display_name":"Augmenting JPEG2000 With Wavelet Coefficient Prediction","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3089737543","doi":"https://doi.org/10.1109/icip40778.2020.9190969","mag":"3089737543"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9190969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5089940466","display_name":"Antoni Dimitriadis","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Antoni Dimitriadis","raw_affiliation_strings":["School of Electrical Engineering & Telecommunications, University of New South Wales, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering & Telecommunications, University of New South Wales, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056716947","display_name":"David Taubman","orcid":"https://orcid.org/0000-0002-8458-6402"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"David Taubman","raw_affiliation_strings":["School of Electrical Engineering & Telecommunications, University of New South Wales, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering & Telecommunications, University of New South Wales, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I31746571"],"apc_list":null,"apc_paid":null,"fwci":0.0979,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.41098112,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1276","last_page":"1280"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998999834060669,"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/T10688","display_name":"Image and Signal Denoising Methods","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/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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9994000196456909,"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/jpeg-2000","display_name":"JPEG 2000","score":0.8832409381866455},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.6431307792663574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6333619356155396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6075565218925476},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5894542336463928},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.5885709524154663},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.5712030529975891},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.4715445339679718},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43692609667778015},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.43523073196411133},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4349686801433563},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4342223107814789},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3836187720298767},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.25847622752189636},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.13174596428871155}],"concepts":[{"id":"https://openalex.org/C69216139","wikidata":"https://www.wikidata.org/wiki/Q931783","display_name":"JPEG 2000","level":5,"score":0.8832409381866455},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.6431307792663574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6333619356155396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6075565218925476},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5894542336463928},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.5885709524154663},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.5712030529975891},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.4715445339679718},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43692609667778015},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.43523073196411133},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4349686801433563},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4342223107814789},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3836187720298767},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25847622752189636},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.13174596428871155}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9190969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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":8,"referenced_works":["https://openalex.org/W193429199","https://openalex.org/W2039760575","https://openalex.org/W2067042811","https://openalex.org/W2121058967","https://openalex.org/W2121927366","https://openalex.org/W2739757502","https://openalex.org/W2915130236","https://openalex.org/W2954506705"],"related_works":["https://openalex.org/W3092983664","https://openalex.org/W2390873329","https://openalex.org/W2151403598","https://openalex.org/W2385039112","https://openalex.org/W2137460601","https://openalex.org/W2207053045","https://openalex.org/W2354616978","https://openalex.org/W2357744830","https://openalex.org/W2062001860","https://openalex.org/W1575921859"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,19,111,120],"novel":[4],"modification":[5,17],"to":[6,22,64,116,126],"the":[7,13,24,28,35,66,69,76,79,85,88,104,129],"Discrete":[8],"Wavelet":[9],"Transform":[10],"used":[11],"in":[12,122],"JPEG2000":[14,130],"standard.":[15,131],"The":[16],"utilises":[18],"learning-based":[20],"method":[21,52],"predict":[23],"detail":[25],"coefficients":[26,30],"from":[27],"approximation":[29],"at":[31,101],"each":[32],"level":[33],"of":[34,45,68,78,87,114,124],"transform,":[36],"allowing":[37],"for":[38],"higher":[39],"compression":[40,70],"rates":[41],"while":[42],"maintaining":[43],"most":[44],"JPEG2000's":[46],"appealing":[47],"features.":[48],"A":[49],"coefficient":[50],"prediction":[51,80],"based":[53],"on":[54,84],"single-image":[55],"super":[56],"resolution":[57],"literature":[58],"has":[59],"been":[60],"implemented":[61],"and":[62,97],"tested":[63],"assess":[65],"potential":[67],"system.":[71],"We":[72],"have":[73],"observed":[74],"that":[75],"accuracy":[77],"is":[81],"highly":[82,95],"dependent":[83],"content":[86],"test":[89,107],"image,":[90],"with":[91],"certain":[92],"features":[93],"being":[94],"predictable":[96,100],"others":[98],"not":[99],"all.":[102],"In":[103],"best":[105],"performing":[106],"images,":[108],"we":[109],"see":[110],"quality":[112],"gain":[113],"up":[115,125],"1.5dB":[117],"or":[118],"equivalently":[119],"reduction":[121],"bit-rate":[123],"14%":[127],"over":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}