{"id":"https://openalex.org/W3045328262","doi":"https://doi.org/10.48550/arxiv.2007.12055","title":"Improving distribution and flexible quantization for DCT coefficients","display_name":"Improving distribution and flexible quantization for DCT coefficients","publication_year":2020,"publication_date":"2020-07-23","ids":{"openalex":"https://openalex.org/W3045328262","doi":"https://doi.org/10.48550/arxiv.2007.12055","mag":"3045328262"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2007.12055","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.12055","pdf_url":"https://arxiv.org/pdf/2007.12055","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.12055","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109420627","display_name":"Jarek Duda","orcid":"https://orcid.org/0000-0001-9559-809X","observed_orcids":["https://orcid.org/0000-0001-9559-809X"]},"institutions":[{"id":"https://openalex.org/I126596746","display_name":"Jagiellonian University","ror":"https://ror.org/03bqmcz70","country_code":"PL","type":"education","lineage":["https://openalex.org/I126596746"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Duda, Jarek","raw_affiliation_strings":["Jagiellonian University , Golebia 24 , 31-007 Krakow , Poland,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jagiellonian University , Golebia 24 , 31-007 Krakow , Poland,","institution_ids":["https://openalex.org/I126596746"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5109420627"],"corresponding_institution_ids":["https://openalex.org/I126596746"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9984999895095825,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9984999895095825,"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.0013000000035390258,"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"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":9.999999747378752e-05,"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/dct-coefficients","display_name":"DCT coefficients","score":0.8510000109672546},{"id":"https://openalex.org/keywords/jpeg-compression","display_name":"JPEG compression","score":0.7990000247955322},{"id":"https://openalex.org/keywords/exponential-power-distribution","display_name":"exponential power distribution","score":0.8539999723434448},{"id":"https://openalex.org/keywords/laplace-distribution","display_name":"Laplace distribution","score":0.9390000104904175},{"id":"https://openalex.org/keywords/non-uniform-quantization","display_name":"non-uniform quantization","score":0.9340000152587891},{"id":"https://openalex.org/keywords/rate-distortion-optimization","display_name":"rate-distortion optimization","score":0.921999990940094},{"id":"https://openalex.org/keywords/blocking-artifacts","display_name":"blocking artifacts","score":0.6779999732971191},{"id":"https://openalex.org/keywords/distribution-prediction","display_name":"distribution prediction","score":0.4309999942779541}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.706507682800293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6193500757217407},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.5644651651382446},{"id":"https://openalex.org/C2777894999","wikidata":"https://www.wikidata.org/wiki/Q4781758","display_name":"Approx","level":2,"score":0.49061575531959534},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4713439643383026},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4558802843093872},{"id":"https://openalex.org/C2778049214","wikidata":"https://www.wikidata.org/wiki/Q7512234","display_name":"Sigma","level":2,"score":0.43126043677330017},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.41714906692504883},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.2905387878417969},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2708384692668915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.21044111251831055},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16212335228919983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13742324709892273},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.1181376576423645},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2007.12055","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.12055","pdf_url":"https://arxiv.org/pdf/2007.12055","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3045328262","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2007.12055","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2007.12055","doi":"https://doi.org/10.48550/arxiv.2007.12055","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2007.12055","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.12055","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.12055","pdf_url":"https://arxiv.org/pdf/2007.12055","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1834553487","https://openalex.org/W2054323570","https://openalex.org/W2063678710","https://openalex.org/W2094007622","https://openalex.org/W2109808436","https://openalex.org/W2126479355","https://openalex.org/W2140196014","https://openalex.org/W2150593711","https://openalex.org/W2615675654","https://openalex.org/W2948676599","https://openalex.org/W3009289987","https://openalex.org/W3015990706"],"related_works":["https://openalex.org/W2956363439","https://openalex.org/W1997343482","https://openalex.org/W2249817159","https://openalex.org/W1507416859","https://openalex.org/W2818945729","https://openalex.org/W2115381009","https://openalex.org/W2751246769","https://openalex.org/W2145429552","https://openalex.org/W2560027734","https://openalex.org/W2199115549","https://openalex.org/W2827850253","https://openalex.org/W2810689380","https://openalex.org/W2159523357","https://openalex.org/W2274068886","https://openalex.org/W2093110897","https://openalex.org/W3133259983","https://openalex.org/W1966160594","https://openalex.org/W2143976439","https://openalex.org/W2028791311","https://openalex.org/W1894911367"],"abstract_inverted_index":{"While":[0],"it":[1],"is":[2,66,130],"a":[3],"common":[4],"knowledge":[5],"that":[6],"AC":[7],"coefficients":[8,78,82],"of":[9,14,43,109,163,168,187],"Fourier-related":[10],"transforms,":[11],"like":[12],"DCT-II":[13],"JPEG":[15],"image":[16],"compression,":[17],"are":[18],"from":[19,79,93],"Laplace":[20,44],"distribution,":[21],"there":[22,129],"was":[23],"tested":[24],"more":[25,192],"general":[26],"EPD":[27],"(exponential":[28],"power":[29],"distribution)":[30],"$\u03c1\\sim":[31],"\\exp(-(|x-\u03bc|/\u03c3)^\u03ba)$":[32],"family,":[33],"leading":[34,199],"to":[35,61,97,181,191,200],"maximum":[36],"likelihood":[37],"estimated":[38],"(MLE)":[39],"$\u03ba\\approx":[40],"0.5$":[41,117],"instead":[42],"distribution":[45,146],"$\u03ba=1$":[46],"-":[47,105,157,167],"such":[48,126],"replacement":[49],"gives":[50,154],"$\\approx":[51,116],"0.1$":[52],"bits/value":[53,118],"mean":[54,119],"savings":[55,120],"(per":[56],"pixel":[57],"for":[58,63,76,106,125,159,177],"grayscale,":[59],"up":[60],"$3\\times$":[62],"RGB).":[64],"There":[65],"also":[67,101,131],"discussed":[68,132],"predicting":[69],"distributions":[70],"(as":[71],"$\u03bc,":[72],"\u03c3,":[73],"\u03ba$":[74],"parameters)":[75],"DCT":[77,88],"already":[80],"decoded":[81],"in":[83],"the":[84],"current":[85],"and":[86],"neighboring":[87,94],"blocks.":[89],"Predicting":[90],"values":[91],"$(\u03bc)$":[92],"blocks":[95],"allows":[96],"reduce":[98],"blocking":[99],"artifacts,":[100],"improve":[102],"compression":[103],"ratio":[104],"which":[107,142],"prediction":[108],"uncertainty/width":[110],"$\u03c3$":[111],"alone":[112,179],"provides":[113],"much":[114],"larger":[115],"opportunity":[121],"(often":[122],"neglected).":[123],"Especially":[124],"continuous":[127,137],"distributions,":[128],"quantization":[133,155,166,203],"approach":[134],"through":[135],"optimized":[136,164],"\\emph{quantization":[138],"density":[139],"function}":[140],"$q$,":[141],"inverse":[143],"CDF":[144],"(cumulative":[145],"function)":[147],"$Q$":[148],"on":[149],"regular":[150],"lattice":[151],"$\\{Q^{-1}((i-1/2)/N):i=1\\ldots":[152],"N\\}$":[153],"nodes":[156],"allowing":[158],"flexible":[160],"inexpensive":[161],"choice":[162],"(non-uniform)":[165],"varying":[169],"size":[170],"$N$,":[171],"with":[172,205],"rate-distortion":[173],"control.":[174],"Optimizing":[175,195],"$q$":[176],"distortion":[178],"leads":[180],"significant":[182],"improvement,":[183],"however,":[184],"at":[185],"cost":[186],"increased":[188],"entropy":[189],"due":[190],"uniform":[193,202],"distribution.":[194],"both":[196],"turns":[197],"out":[198],"nearly":[201],"here,":[204],"automatized":[206],"tail":[207],"handling.":[208]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2025-10-10T00:00:00"}
