{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/context-adaptive-entropy-model-for-end-to-end","title":"Context-adaptive Entropy Model for End-to-end Optimized Image Compression","arxiv_id":"1809.10452","date":"2018-09-27","proceeding":"ICLR 2019 5","authors":["Jooyoung Lee","Seunghyun Cho","Seung-Kwon Beack"],"abstract":"We propose a context-adaptive entropy model for use in end-to-end optimized\nimage compression. Our model exploits two types of contexts, bit-consuming\ncontexts and bit-free contexts, distinguished based upon whether additional bit\nallocation is required. Based on these contexts, we allow the model to more\naccurately estimate the distribution of each latent representation with a more\ngeneralized form of the approximation models, which accordingly leads to an\nenhanced compression performance. Based on the experimental results, the\nproposed method outperforms the traditional image codecs, such as BPG and\nJPEG2000, as well as other previous artificial-neural-network (ANN) based\napproaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale\nstructural similarity (MS-SSIM) index.","url_abs":"http://arxiv.org/abs/1809.10452v3","url_pdf":"http://arxiv.org/pdf/1809.10452v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"context-adaptive-entropy-model-for-end-to-end","repo_url":"https://github.com/JooyoungLeeETRI/CA_Entropy_Model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"context-adaptive-entropy-model-for-end-to-end","repo_url":"https://github.com/RenYang-home/OpenDVC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"image-compression","task_name":"Image Compression"},{"task_slug":"ms-ssim","task_name":"MS-SSIM"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.10452","atlas_url":"https://app.syntology.ai/?focus=1809.10452","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}