{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T05:37:15Z","timestamp":1737005835970,"version":"3.33.0"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tip.2024.3522813","type":"journal-article","created":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T19:47:54Z","timestamp":1735760874000},"page":"306-319","source":"Crossref","is-referenced-by-count":0,"title":["Exploiting Latent Properties to Optimize Neural Codecs"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1428-4297","authenticated-orcid":false,"given":"Muhammet","family":"Balcilar","sequence":"first","affiliation":[{"name":"InterDigital Inc., Cesson-S&#x00E9;vign&#x00E9;, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-017X","authenticated-orcid":false,"given":"Bharath","family":"Bhushan Damodaran","sequence":"additional","affiliation":[{"name":"InterDigital Inc., Cesson-S&#x00E9;vign&#x00E9;, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karam","family":"Naser","sequence":"additional","affiliation":[{"name":"InterDigital Inc., Cesson-S&#x00E9;vign&#x00E9;, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2123-7819","authenticated-orcid":false,"given":"Franck","family":"Galpin","sequence":"additional","affiliation":[{"name":"InterDigital Inc., Cesson-S&#x00E9;vign&#x00E9;, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Hellier","sequence":"additional","affiliation":[{"name":"University of Rennes, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00713"},{"key":"ref2","first-page":"675","article-title":"Rethinking lossy compression: The rate-distortion-perception tradeoff","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","volume":"97","author":"Blau"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref4","article-title":"Lossy image compression with compressive autoencoders","author":"Theis","year":"2017","journal-title":"arXiv:1703.00395"},{"key":"ref5","article-title":"End-to-end optimized image compression","author":"Ball\u00e9","year":"2017","journal-title":"arXiv:1611.01704"},{"article-title":"Variational image compression with a scale hyperprior","volume-title":"Proc. ICLR","author":"Ball\u00e9","key":"ref6"},{"key":"ref7","first-page":"10794","article-title":"Joint autoregressive and hierarchical priors for learned image compression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Minnen"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190935"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00796"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475213"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01453"},{"key":"ref12","article-title":"Learning accurate entropy model with global reference for image compression","author":"Qian","year":"2021","journal-title":"arXiv:2010.08321"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00590"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01383"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01126"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00853"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897240"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3138300"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00661"},{"key":"ref20","first-page":"18114","article-title":"Deep contextual video compression","volume":"34","author":"Li","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547845"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-3626-0"},{"key":"ref23","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.","volume":"30","author":"Agustsson"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01709"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00591"},{"key":"ref26","first-page":"1","article-title":"LVQ-VAE: End-to-end hyperprior-based variational image compression with lattice vector quantization","volume-title":"Proc. ICLR","author":"Kudo"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00987"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/PCS56426.2022.10018064"},{"key":"ref29","first-page":"573","article-title":"Improving inference for neural image compression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Yang"},{"key":"ref30","first-page":"3920","article-title":"Soft then hard: Rethinking the quantization in neural image compression","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref31","first-page":"6","article-title":"Content adaptive optimization for neural image compression","volume-title":"Proc. CVPR Workshops","author":"Campos"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_27"},{"key":"ref33","article-title":"Overfitting for fun and profit: Instance-adaptive data compression","author":"van Rozendaal","year":"2021","journal-title":"arXiv:2101.08687"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP49359.2023.10222469"},{"volume-title":"Ee2-4.1: Shifting Quantization Center","year":"2023","author":"Balcilar","key":"ref35"},{"key":"ref36","article-title":"Asymmetric numeral systems","author":"Duda","year":"2009","journal-title":"arXiv:0902.0271"},{"key":"ref37","first-page":"12367","article-title":"Universally quantized neural compression","volume-title":"Proc. Adv. neural Inf. Process. Syst.","volume":"33","author":"Agustsson"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1984.1056920"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01340.x"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548219"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/jstsp.2020.3034501"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056457"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1979.1056067"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC50000.2020.9219581"},{"key":"ref45","volume":"12","author":"Miettinen","year":"2012","journal-title":"Nonlinear Multiobjective Optimization"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.crma.2012.03.014"},{"key":"ref47","article-title":"CompressAI: A PyTorch library and evaluation platform for end-to-end compression research","author":"B\u00e9gaint","year":"2020","journal-title":"arXiv:2011.03029"},{"volume-title":"Kodak Lossless True Color Image Suite (PhotoCD PCD0992)","year":"2024","author":"Kodak","key":"ref48"},{"volume-title":"CLIC: Challenge on Learned Image Compression","year":"2024","key":"ref49"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3339825.3394937"},{"article-title":"Calculation of Average PSNR Differences between RD curves","volume-title":"Proc. 13th VCEG Meeting ITU-T SG16\/Q6","author":"Bjontegaard","key":"ref51"},{"volume-title":"Common Test Conditions and Evaluation Procedures for Enhanced Compression Tool Testing","year":"2022","author":"Karczewicz","key":"ref52"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2002.800499"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056484"},{"volume-title":"Algorithm Description of Enhanced Compression Model 9 (ECM 9)","year":"2023","author":"Coban","key":"ref55"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/83\/10795784\/10820063.pdf?arnumber=10820063","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T20:27:21Z","timestamp":1736972841000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10820063\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/tip.2024.3522813","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"type":"print","value":"1057-7149"},{"type":"electronic","value":"1941-0042"}],"subject":[],"published":{"date-parts":[[2025]]}}}