{"id":"https://openalex.org/W2163814577","doi":"https://doi.org/10.1109/icip.2008.4711934","title":"Lossless image compression using 2D allpass filters","display_name":"Lossless image compression using 2D allpass filters","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2163814577","doi":"https://doi.org/10.1109/icip.2008.4711934","mag":"2163814577"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2008.4711934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2008.4711934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 15th IEEE International Conference on Image Processing","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/A5100430884","display_name":"Xi Zhang","orcid":"https://orcid.org/0009-0004-8887-0154"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Xi Zhang","raw_affiliation_strings":["Department of Information and Communication Engineering, University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111972504","display_name":"Kosuke Ohno","orcid":null},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kosuke Ohno","raw_affiliation_strings":["Department of Information and Communication Engineering, University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100430884"],"corresponding_institution_ids":["https://openalex.org/I20529979"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.16826779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"85","issue":null,"first_page":"1033","last_page":"1036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9995999932289124,"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/T11034","display_name":"Digital Filter Design and Implementation","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/lossless-compression","display_name":"Lossless compression","score":0.7193177938461304},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.6841820478439331},{"id":"https://openalex.org/keywords/second-generation-wavelet-transform","display_name":"Second-generation wavelet transform","score":0.6273057460784912},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.6173990964889526},{"id":"https://openalex.org/keywords/stationary-wavelet-transform","display_name":"Stationary wavelet transform","score":0.5857129096984863},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.5758572816848755},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.5432125926017761},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5427380800247192},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5380908846855164},{"id":"https://openalex.org/keywords/lifting-scheme","display_name":"Lifting scheme","score":0.5214300155639648},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.518761157989502},{"id":"https://openalex.org/keywords/harmonic-wavelet-transform","display_name":"Harmonic wavelet transform","score":0.4613834023475647},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.4150252640247345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38115906715393066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3778762221336365},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3642094135284424},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.1555207073688507},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.13982847332954407},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11608752608299255}],"concepts":[{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.7193177938461304},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.6841820478439331},{"id":"https://openalex.org/C111350171","wikidata":"https://www.wikidata.org/wiki/Q7443700","display_name":"Second-generation wavelet transform","level":5,"score":0.6273057460784912},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.6173990964889526},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.5857129096984863},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.5758572816848755},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.5432125926017761},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5427380800247192},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5380908846855164},{"id":"https://openalex.org/C199550912","wikidata":"https://www.wikidata.org/wiki/Q3238415","display_name":"Lifting scheme","level":5,"score":0.5214300155639648},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.518761157989502},{"id":"https://openalex.org/C1109138","wikidata":"https://www.wikidata.org/wiki/Q3280930","display_name":"Harmonic wavelet transform","level":5,"score":0.4613834023475647},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.4150252640247345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38115906715393066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3778762221336365},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3642094135284424},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.1555207073688507},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.13982847332954407},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11608752608299255}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip.2008.4711934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2008.4711934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 15th IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.565.9774","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.565.9774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.xiz.ice.uec.ac.jp/paper/icip08.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W624086531","https://openalex.org/W1975358183","https://openalex.org/W2020919250","https://openalex.org/W2047449921","https://openalex.org/W2062024414","https://openalex.org/W2101789093","https://openalex.org/W2102740494","https://openalex.org/W2103880866","https://openalex.org/W2105701401","https://openalex.org/W2117188745","https://openalex.org/W2118217749","https://openalex.org/W2138674095","https://openalex.org/W2148593155","https://openalex.org/W2163372673","https://openalex.org/W2501356853","https://openalex.org/W4237616289","https://openalex.org/W4255272544","https://openalex.org/W4302366751","https://openalex.org/W6683746756"],"related_works":["https://openalex.org/W1588899229","https://openalex.org/W1976022598","https://openalex.org/W2386482837","https://openalex.org/W2355889335","https://openalex.org/W2023142747","https://openalex.org/W2085792030","https://openalex.org/W2019515987","https://openalex.org/W2025989299","https://openalex.org/W3116939719","https://openalex.org/W1967182499"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3],"reversible":[4,28,55,80],"integer-to-integer":[5],"wavelet":[6,29,56,66,81,93,98],"transform":[7,30,57,82,99],"based":[8],"on":[9],"non-separable":[10,35,78],"2D":[11,36,79],"allpass":[12,37],"filters":[13],"is":[14,31,41,58,69],"proposed":[15,54,77],"for":[16],"lossless":[17,49,86],"image":[18],"coding.":[19],"The":[20,48],"number":[21],"of":[22,52],"rounding":[23],"operations":[24],"included":[25],"in":[26,100],"the":[27,53,63,72,76,90,96,101],"reduced":[32],"by":[33],"using":[34],"filters,":[38],"thus":[39],"it":[40],"expected":[42],"to":[43],"get":[44],"better":[45,85],"coding":[46,50,87],"performance.":[47],"performance":[51,88],"evaluated":[59],"and":[60],"compared":[61],"with":[62],"conventional":[64,91],"separable":[65,92],"transforms.":[67],"It":[68],"shown":[70],"from":[71],"experimental":[73],"results":[74],"that":[75],"can":[83],"achieve":[84],"than":[89],"transforms,":[94],"including":[95],"D-5/3":[97],"JPEG":[102],"2000.":[103]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
