{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:39:15Z","timestamp":1757630355000,"version":"3.44.0"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371288","62301299","62320106003","U24A20251","62431017","62125109","62120106007","62401357","62401366"],"award-info":[{"award-number":["62371288","62301299","62320106003","U24A20251","62431017","62125109","62120106007","62401357","62401366"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Program of Shanghai Science and Technology Innovation Project","doi-asserted-by":"publisher","award":["24BC3200800"],"award-info":[{"award-number":["24BC3200800"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1109\/tcsvt.2025.3551780","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T13:45:32Z","timestamp":1742305532000},"page":"9443-9459","source":"Crossref","is-referenced-by-count":0,"title":["Generative Probabilistic Entropy Modeling With Conditional Diffusion for Learned Image Compression"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9275-2095","authenticated-orcid":false,"given":"Maida","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2522-5778","authenticated-orcid":false,"given":"Wenrui","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9650-8874","authenticated-orcid":false,"given":"Shaohui","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2888-594X","authenticated-orcid":false,"given":"Chenglin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-9880","authenticated-orcid":false,"given":"Junni","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6168-2688","authenticated-orcid":false,"given":"Weisheng","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4552-0029","authenticated-orcid":false,"given":"Hongkai","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/79.733497"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/103085.103089"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0923-5965(01)00024-8"},{"volume-title":"BPG Image Format","year":"2015","author":"Bellard","key":"ref4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3101953"},{"key":"ref6","article-title":"Video compression beyond VVC: Quantitative analysis of intra coding tools in enhanced compression model (ECM)","author":"Abdoli","year":"2024","journal-title":"arXiv:2404.07872"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/79.952802"},{"article-title":"End-to-end optimized image compression","volume-title":"Proc. 5th Int. Conf. Learn. Rep.","author":"Ball\u00e9","key":"ref8"},{"key":"ref9","first-page":"1141","article-title":"Soft-to-hard vector quantization for end-to-end learning compressible representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Agustsson"},{"article-title":"Variational image compression with a scale hyperprior","volume-title":"Proc. 6th Int. Conf. Learn. Rep.","author":"Ball\u00e9","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3065339"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00590"},{"article-title":"Context-adaptive entropy model for End-to-end optimized image compression","volume-title":"Proc. 7th Int. Conf. Learn. Rep.","author":"Lee","key":"ref13"},{"key":"ref14","first-page":"10771","article-title":"Joint autoregressive and hierarchical priors for learned image compression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Minnen"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00796"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01453"},{"article-title":"Learning accurate entropy model with global reference for image compression","volume-title":"Proc. 9th Int. Conf. Learn. Rep.","author":"Qian","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2022.3209661"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190935"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2985225"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3058615"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00563"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3089491"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01697"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475213"},{"key":"ref26","article-title":"High-efficiency lossy image coding through adaptive neighborhood information aggregation","author":"Lu","year":"2022","journal-title":"arXiv:2204.11448"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01686"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20308"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19797-0_32"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01709"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19800-7_26"},{"article-title":"Entroformer: A transformer-based entropy model for learned image compression","volume-title":"Proc. 10th Int. Conf. Learn. Rep.","author":"Qian","key":"ref32"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3263099"},{"article-title":"Transformer-based transform coding","volume-title":"Proc. 10th Int. Conf. Learn. Rep.","author":"Zhu","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3237274"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3199472"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3216713"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.3035680"},{"article-title":"Frequency-aware transformer for learned image compression","volume-title":"Proc. 12th Int. Conf. Learn. Rep.","author":"Li","key":"ref39"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01383"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00987"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00591"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref44","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Nichol"},{"key":"ref45","first-page":"12438","article-title":"Improved techniques for training score-based generative models","volume-title":"Proc. NIPS","volume":"33","author":"Song"},{"article-title":"Denoising diffusion implicit models","volume-title":"Proc. 9th Int. Conf. Learn. Rep.","author":"Song","key":"ref46"},{"key":"ref47","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein"},{"key":"ref48","first-page":"11918","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Song"},{"article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. 9th Int. Conf. Learn. Rep.","author":"Song","key":"ref49"},{"key":"ref50","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. NIPS","volume":"33","author":"Ho"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00462"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"ref54","article-title":"DiffWave: A versatile diffusion model for audio synthesis","author":"Kong","year":"2020","journal-title":"arXiv:2009.09761"},{"key":"ref55","first-page":"24804","article-title":"CSDI: Conditional score-based diffusion models for probabilistic time series imputation","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Tashiro"},{"key":"ref56","article-title":"Lossy compression with Gaussian diffusion","author":"Theis","year":"2022","journal-title":"arXiv:2206.08889"},{"article-title":"Step-unrolled denoising autoencoders for text generation","volume-title":"Proc. 9th Int. Conf. Learn. Rep.","author":"Savinov","key":"ref57"},{"key":"ref58","first-page":"12454","article-title":"Argmax flows and multinomial diffusion: Learning categorical distributions","volume-title":"Proc. NIPS","author":"Hoogeboom"},{"key":"ref59","article-title":"Lossy image compression with conditional diffusion models","author":"Yang","year":"2022","journal-title":"arXiv:2209.06950"},{"key":"ref60","article-title":"A residual diffusion model for high perceptual quality codec augmentation","author":"Fathima Ghouse","year":"2023","journal-title":"arXiv:2301.05489"},{"key":"ref61","article-title":"Extreme generative image compression by learning text embedding from diffusion models","author":"Pan","year":"2022","journal-title":"arXiv:2211.07793"},{"article-title":"Auto-encoding variational Bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Rep.","author":"Kingma","key":"ref62"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"volume-title":"Kodak Lossless True Color Image Suite (photocd Pcd0992)","year":"1993","author":"Kodak","key":"ref64"},{"volume-title":"Workshop and Challenge on Learned Image Compression (CLIC)","year":"2020","key":"ref65"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.2312\/stag.20141242"},{"article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Rep.","author":"Kingma","key":"ref67"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref69","first-page":"18114","article-title":"Deep contextual video compression","volume-title":"Proc. Adv. Neural Inf. Process. Syst. 34","author":"Li"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3220421"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-01144-2"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3339825.3394937"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7532610"},{"key":"ref74","article-title":"Common test conditions and software reference configurations","volume-title":"Proc. JCTVC","volume":"12","author":"Bossen"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/76\/11154820\/10929015.pdf?arnumber=10929015","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T17:49:06Z","timestamp":1757526546000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10929015\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":74,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2025.3551780","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"type":"print","value":"1051-8215"},{"type":"electronic","value":"1558-2205"}],"subject":[],"published":{"date-parts":[[2025,9]]}}}