{"id":"https://openalex.org/W4320175697","doi":"https://doi.org/10.48550/arxiv.2206.05279","title":"PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework","display_name":"PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework","publication_year":2022,"publication_date":"2022-06-10","ids":{"openalex":"https://openalex.org/W4320175697","doi":"https://doi.org/10.48550/arxiv.2206.05279"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2206.05279","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.05279","pdf_url":"https://arxiv.org/pdf/2206.05279","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/2206.05279","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kang, Ning","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang, Ning","raw_affiliation_strings":["Huawei Noah's Ark Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017092538","display_name":"Shanzhao Qiu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiu, Shanzhao","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100757125","display_name":"Shifeng Zhang","orcid":"https://orcid.org/0000-0001-8305-9566","observed_orcids":["https://orcid.org/0000-0001-8305-9566"]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Shifeng","raw_affiliation_strings":["Huawei Noah's Ark Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077862962","display_name":"Zhenguo Li","orcid":"https://orcid.org/0009-0003-6369-0213","observed_orcids":["https://orcid.org/0009-0003-6369-0213","https://orcid.org/0009-0000-8773-0687"]},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li, Zhenguo","raw_affiliation_strings":["Huawei Noah's Ark Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034104790","display_name":"Shu\u2010Tao Xia","orcid":"https://orcid.org/0000-0002-8639-982X","observed_orcids":["https://orcid.org/0000-0002-8639-982X"]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia, Shutao","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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":0.0006000000284984708,"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/T11269","display_name":"Algorithms and Data Compression","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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-models","display_name":"autoregressive models","score":0.746999979019165},{"id":"https://openalex.org/keywords/vq-vae","display_name":"vector quantized variational autoencoder","score":0.9079999923706055},{"id":"https://openalex.org/keywords/entropy-coding","display_name":"entropy coding","score":0.7889999747276306},{"id":"https://openalex.org/keywords/compression-throughput","display_name":"compression throughput","score":0.7730000019073486}],"concepts":[{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.8396143317222595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8036662340164185},{"id":"https://openalex.org/C161765866","wikidata":"https://www.wikidata.org/wiki/Q184748","display_name":"Codec","level":2,"score":0.6526939868927002},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.6502128839492798},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.5652092099189758},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5491580367088318},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5043925046920776},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.45183736085891724},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.380199670791626},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3771386742591858},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3733537793159485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36787885427474976},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.22677311301231384},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.21117064356803894},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2206.05279","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.05279","pdf_url":"https://arxiv.org/pdf/2206.05279","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2206.05279","doi":"https://doi.org/10.48550/arxiv.2206.05279","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2206.05279","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.05279","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.05279","pdf_url":"https://arxiv.org/pdf/2206.05279","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/7","score":0.4699999988079071,"display_name":"Affordable and clean energy"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320175697.pdf","grobid_xml":"https://content.openalex.org/works/W4320175697.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3210332869","https://openalex.org/W4327499886","https://openalex.org/W4294006383","https://openalex.org/W3202870363","https://openalex.org/W1680283075","https://openalex.org/W2115343698","https://openalex.org/W2380116549","https://openalex.org/W2096442341","https://openalex.org/W2386792074","https://openalex.org/W3180760233"],"abstract_inverted_index":{"Generative":[0],"model":[1,97],"based":[2],"image":[3,55],"lossless":[4,56],"compression":[5,14,57,65,147],"algorithms":[6],"have":[7],"seen":[8],"a":[9,69,109,130],"great":[10],"success":[11],"in":[12,103,134],"improving":[13],"ratio.":[15],"However,":[16],"the":[17,30,79],"throughput":[18],"for":[19,63],"most":[20,31,39,80],"of":[21,132],"them":[22,37],"is":[23,140],"less":[24],"than":[25,78,127],"1":[26],"MB/s":[27,62],"even":[28],"with":[29,68,117],"advanced":[32],"AI":[33,92,146],"accelerated":[34],"chips,":[35],"preventing":[36],"from":[38],"real-world":[40],"applications,":[41],"which":[42,100],"often":[43],"require":[44],"100":[45],"MB/s.":[46],"In":[47],"this":[48,86,139],"paper,":[49],"we":[50,88,107],"propose":[51],"PILC,":[52],"an":[53,91,141],"end-to-end":[54],"framework":[58,124],"that":[59,94,114,122],"achieves":[60],"200":[61],"both":[64],"and":[66,98],"decompression":[67],"single":[70],"NVIDIA":[71],"Tesla":[72],"V100":[73],"GPU,":[74],"10":[75],"times":[76],"faster":[77],"efficient":[81],"one":[82],"before.":[83],"To":[84],"obtain":[85],"result,":[87],"first":[89],"develop":[90],"codec":[93],"combines":[95],"auto-regressive":[96],"VQ-VAE":[99],"performs":[101],"well":[102,116],"lightweight":[104],"setting,":[105],"then":[106],"design":[108],"low":[110],"complexity":[111],"entropy":[112],"coder":[113],"works":[115],"our":[118,123],"codec.":[119],"Experiments":[120],"show":[121],"compresses":[125],"better":[126],"PNG":[128],"by":[129],"margin":[131],"30%":[133],"multiple":[135],"datasets.":[136],"We":[137],"believe":[138],"important":[142],"step":[143],"to":[144,149],"bring":[145],"forward":[148],"commercial":[150],"use.":[151]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2023-02-13T00:00:00"}
