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But unlike traditional codec, it lacks a universal optimization method to seek efficient representation for different images. In this paper, we develop a plug-and-play optimization framework for seeking higher compression ratio, which can be flexibly applied to existing and potential future compression networks. To make the latent representation more efficient, we propose a novel latent optimization algorithm to adaptively remove the redundancy for each image. Additionally, inspired by the potential of side information for traditional codecs, we introduce side information into our framework, and integrate side information optimization with latent optimization to further enhance the compression ratio. In particular, with the joint side information and latent optimization, we can achieve fine rate control using only single model instead of training different models for different rate-distortion trade-offs, which significantly reduces the training and storage cost to support multiple bit rates. Experimental results demonstrate that our proposed framework can remarkably boost the machine learning-based compression ratio, achieving more than 10% additional bit rate saving on three different representative network structures. With the proposed optimization framework, we can achieve 7.6% bit rate saving against the latest traditional coding standard VVC on Kodak dataset, yielding the state-of-the-art compression ratio.<\/jats:p>","DOI":"10.1145\/3580499","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T12:10:44Z","timestamp":1688386244000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A Universal Optimization Framework for Learning-based Image Codec"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8413-9979","authenticated-orcid":false,"given":"Jing","family":"Zhao","sequence":"first","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5635-2916","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4559-0086","authenticated-orcid":false,"given":"Jiahao","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9796-0478","authenticated-orcid":false,"given":"Ruiqin","family":"Xiong","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5383-6424","authenticated-orcid":false,"given":"Yan","family":"Lu","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1141","article-title":"Soft-to-hard vector quantization for end-to-end learning compressible representations","author":"Agustsson Eirikur","year":"2017","unstructured":"Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, and Luc Van Gool. 2017. 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