{"id":"https://openalex.org/W4408564084","doi":"https://doi.org/10.1109/tcsvt.2025.3551780","title":"Generative Probabilistic Entropy Modeling With Conditional Diffusion for Learned Image Compression","display_name":"Generative Probabilistic Entropy Modeling With Conditional Diffusion for Learned Image Compression","publication_year":2025,"publication_date":"2025-03-18","ids":{"openalex":"https://openalex.org/W4408564084","doi":"https://doi.org/10.1109/tcsvt.2025.3551780"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2025.3551780","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2025.3551780","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-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/A5086886949","display_name":"Maida Cao","orcid":"https://orcid.org/0000-0002-9275-2095"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maida Cao","raw_affiliation_strings":["Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-9275-2095","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045342512","display_name":"Wenrui Dai","orcid":"https://orcid.org/0000-0003-2522-5778"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenrui Dai","raw_affiliation_strings":["Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2522-5778","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760682","display_name":"Shaohui Li","orcid":"https://orcid.org/0000-0002-9650-8874"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohui Li","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-9650-8874","affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210114105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100675587","display_name":"Chenglin Li","orcid":"https://orcid.org/0000-0003-2888-594X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglin Li","raw_affiliation_strings":["Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2888-594X","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016006337","display_name":"Junni Zou","orcid":"https://orcid.org/0000-0002-9694-9880"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junni Zou","raw_affiliation_strings":["Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-9694-9880","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015039354","display_name":"Weisheng Hu","orcid":"https://orcid.org/0000-0002-6168-2688"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weisheng Hu","raw_affiliation_strings":["Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6168-2688","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002494284","display_name":"Hongkai Xiong","orcid":"https://orcid.org/0000-0003-4552-0029"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongkai Xiong","raw_affiliation_strings":["Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-4552-0029","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.03553043,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":"9","first_page":"9443","last_page":"9459"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9977999925613403,"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":0.9977999925613403,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9628000259399414,"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":0.9063000082969666,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6097844839096069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5914412140846252},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.581131100654602},{"id":"https://openalex.org/keywords/conditional-entropy","display_name":"Conditional entropy","score":0.5758060216903687},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5440082550048828},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5162815451622009},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.5062743425369263},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.504822313785553},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4849958121776581},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.44949033856391907},{"id":"https://openalex.org/keywords/maximum-entropy-markov-model","display_name":"Maximum-entropy Markov model","score":0.4273946285247803},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.3837057948112488},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3772529661655426},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3543451428413391},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3274803161621094},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27818137407302856},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25457900762557983},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.24908015131950378},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.161756694316864},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.11236360669136047}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6097844839096069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5914412140846252},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.581131100654602},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.5758060216903687},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5440082550048828},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5162815451622009},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.5062743425369263},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.504822313785553},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4849958121776581},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.44949033856391907},{"id":"https://openalex.org/C196956702","wikidata":"https://www.wikidata.org/wiki/Q6795829","display_name":"Maximum-entropy Markov model","level":5,"score":0.4273946285247803},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.3837057948112488},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3772529661655426},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3543451428413391},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3274803161621094},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27818137407302856},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25457900762557983},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.24908015131950378},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.161756694316864},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.11236360669136047},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2025.3551780","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2025.3551780","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1080903817","display_name":null,"funder_award_id":"62320106003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1336459536","display_name":null,"funder_award_id":"24BC3200800","funder_id":"https://openalex.org/F4320336569","funder_display_name":"Shenzhen Science and Technology Innovation Program"},{"id":"https://openalex.org/G2048617456","display_name":null,"funder_award_id":"62301299","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2895017225","display_name":null,"funder_award_id":"U24A20251","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G376436071","display_name":null,"funder_award_id":"62401357","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6094063813","display_name":null,"funder_award_id":"62125109","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6263369104","display_name":null,"funder_award_id":"62431017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6308568386","display_name":null,"funder_award_id":"62401366","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7123918047","display_name":null,"funder_award_id":"62120106007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7931220373","display_name":null,"funder_award_id":"62371288","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336569","display_name":"Shenzhen Science and Technology Innovation Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W2030265202","https://openalex.org/W2040903332","https://openalex.org/W2108598243","https://openalex.org/W2166816543","https://openalex.org/W2169161136","https://openalex.org/W2286454943","https://openalex.org/W2511458122","https://openalex.org/W2769654144","https://openalex.org/W2962785568","https://openalex.org/W3017235483","https://openalex.org/W3031546776","https://openalex.org/W3034469748","https://openalex.org/W3091266734","https://openalex.org/W3095479355","https://openalex.org/W3116647679","https://openalex.org/W3131427579","https://openalex.org/W3134748538","https://openalex.org/W3155072588","https://openalex.org/W3175457126","https://openalex.org/W3202918664","https://openalex.org/W3206996732","https://openalex.org/W4223425316","https://openalex.org/W4226355936","https://openalex.org/W4283817357","https://openalex.org/W4292256660","https://openalex.org/W4292829030","https://openalex.org/W4297095237","https://openalex.org/W4312327562","https://openalex.org/W4312590925","https://openalex.org/W4312597589","https://openalex.org/W4312774595","https://openalex.org/W4312806968","https://openalex.org/W4312933868","https://openalex.org/W4312934587","https://openalex.org/W4312949231","https://openalex.org/W4316660716","https://openalex.org/W4362496227","https://openalex.org/W4386071691","https://openalex.org/W4386075611","https://openalex.org/W4386076451"],"related_works":["https://openalex.org/W1945850644","https://openalex.org/W1896570304","https://openalex.org/W2145913509","https://openalex.org/W2163875935","https://openalex.org/W3125883986","https://openalex.org/W1578813075","https://openalex.org/W2053566994","https://openalex.org/W2803693972","https://openalex.org/W2131865294","https://openalex.org/W202303397"],"abstract_inverted_index":{"Entropy":[0],"modeling":[1,68],"is":[2,115],"the":[3,13,44,61,89,124,156,179,187,204],"core":[4],"component":[5],"of":[6,15,69,94,126],"learned":[7,18,45],"image":[8],"compression":[9],"(LIC)":[10],"that":[11,97,178],"models":[12,31,39],"distribution":[14,125],"latent":[16,46,70,90,127],"representation":[17,47,71,91,128],"from":[19],"input":[20],"images":[21],"via":[22],"neural":[23],"networks":[24],"for":[25,43,60,134],"bit-rate":[26],"estimation.":[27],"However,":[28],"existing":[29,195],"entropy":[30,67,84,181],"employ":[32],"presumed":[33],"parameterized":[34],"distributions":[35,93],"such":[36],"as":[37],"Gaussian":[38],"and":[40,136,164,190],"are":[41,98],"limited":[42],"characterized":[48],"by":[49,100,161,211],"complex":[50],"distributions.":[51],"To":[52],"address":[53],"this":[54,57],"problem,":[55],"in":[56,132,215],"paper,":[58],"we":[59,78,139],"first":[62],"time":[63],"achieve":[64],"generative":[65],"probabilistic":[66,83],"based":[72],"on":[73,186],"conditional":[74,81],"diffusion":[75,106],"models.":[76],"Specifically,":[77],"propose":[79],"a":[80,141],"diffusion-based":[82],"model":[85,108,145,182,206],"(CDPEM)":[86],"to":[87,117,121,146,150,194],"parameterize":[88],"with":[92,129,148,155,171,201],"arbitrary":[95],"forms":[96],"generated":[99],"well":[101],"designed":[102,116],"training-test":[103],"consistent":[104],"denoising":[105],"implicit":[107],"(TC-DDIM)":[109],"without":[110],"introducing":[111],"any":[112],"presumption.":[113],"TC-DDIM":[114,149],"leverage":[118],"ancestral":[119,162],"sampling":[120,163],"gradually":[122],"approximate":[123,157],"guaranteed":[130],"consistency":[131],"generation":[133],"training":[135,173],"test.":[137],"Furthermore,":[138],"develop":[140],"hierarchical":[142],"spatial-channel":[143],"context":[144],"incorporate":[147],"sufficiently":[151],"exploit":[152],"spatial":[153],"correlations":[154,166],"contextual":[158],"information":[159,169],"produced":[160],"channel-wise":[165,168],"using":[167],"aggregation":[170],"reweighted":[172],"loss.":[174],"Experimental":[175],"results":[176],"demonstrate":[177],"proposed":[180,205],"achieves":[183],"state-of-the-art":[184],"performance":[185],"Kodak,":[188],"CLIC,":[189],"Tecnick":[191],"datasets":[192],"compared":[193],"LIC":[196],"methods.":[197],"Remarkably,":[198],"when":[199],"incorporated":[200],"recent":[202],"baselines,":[203],"outperforms":[207],"latest":[208],"VVC":[209],"standard":[210],"an":[212],"evident":[213],"gain":[214],"R-D":[216],"performance.":[217]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
