A survey of learning-based end-to-end video compression

A survey of learning-based end-to-end video compression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A survey of learning-based end-to-end video compression Huanjie He, Yunhui Shi, Jin Wang, You Zuo, Nam Ling, Baocai Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6976730/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the increase of multimedia data and the emergence of intelligent application scenarios such as virtual reality, video compression faces demand for higher resolution and more diverse video data. Compression methods based on end-to end learning have shown great flexibility and certain superiority. Although there are still challenges in computational complexity, artificial intelligence technology has injected more vitality into video compression. The continuous development of visual-language models, artificial intelligence-generated content, and generative models may provide a revolutionary development for compression. Considering these factors, we review new research work and influential articles. Specifically, this paper introduces the development of the video coding group and briefly outlines learning-based image compression methods (intra-frame coding). In particular, we review video compression on different coding frameworks, such as residual and context (inter-frame coding). Finally, we discuss possible future research directions on video compression and the challenges they may face. Deep learning Video compression Image compression Intra-frame coding Inter-frame coding Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6976730","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484437834,"identity":"b9c40b21-8b8c-4bac-86e5-ac6f55bd5664","order_by":0,"name":"Huanjie He","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Huanjie","middleName":"","lastName":"He","suffix":""},{"id":484437835,"identity":"3b0f2f79-3918-47c1-b7c6-b2ba9472bc4a","order_by":1,"name":"Yunhui Shi","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yunhui","middleName":"","lastName":"Shi","suffix":""},{"id":484437838,"identity":"bbb88cd8-d66a-4854-bc66-a68974c0e893","order_by":2,"name":"Jin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACNoaDDWAGvwRDAohmbCBai+QMoJYDxGiBA4MbQIIoLXyMh9se89Tcsdt8u+GZ9AcGG9kNB5ifPSDgsHZjnmPPkrfdOZAmcYAhzXjDATZzAwJa2qRz2A4nm91IAGk5nLjhAA+bBGEt/w4nG88Aa/lPpJbctsN2BhJgLQeI1PK373CCxJ0DyRZnDJKNZx5mM8OrRX7G8WeSM74dtuef3ZN4o6LCTrbvePMzvFoYgI4BgcQGBp4EYOwAmcx41QMBfwOYsmdgYD9ASO0oGAWjYBSMUAAALw9QNRgYjVIAAAAASUVORK5CYII=","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Wang","suffix":""},{"id":484437840,"identity":"fadb47fa-c2fa-43f6-955c-e85d3b58efa1","order_by":3,"name":"You Zuo","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"You","middleName":"","lastName":"Zuo","suffix":""},{"id":484437841,"identity":"09daca0d-e51e-495f-8bf7-10cf708bb67c","order_by":4,"name":"Nam Ling","email":"","orcid":"","institution":"Santa Clara University","correspondingAuthor":false,"prefix":"","firstName":"Nam","middleName":"","lastName":"Ling","suffix":""},{"id":484437842,"identity":"11d96f1a-01de-43d6-a2a8-94994d3a2179","order_by":5,"name":"Baocai Yin","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Baocai","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2025-06-25 16:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6976730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6976730/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89893367,"identity":"7f78908e-2502-437f-9572-0aed34d330f5","added_by":"auto","created_at":"2025-08-26 08:02:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2874240,"visible":true,"origin":"","legend":"","description":"","filename":"Asurveyoflearningbasedendtoendvideocompression.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6976730/v1_covered_ce6a5633-ce7f-4160-8dfa-3bf3f4d84cca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A survey of learning-based end-to-end video compression","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, Video compression, Image compression, Intra-frame coding, Inter-frame coding","lastPublishedDoi":"10.21203/rs.3.rs-6976730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6976730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the increase of multimedia data and the emergence of intelligent application scenarios such as virtual reality, video compression faces demand for higher resolution and more diverse video data. 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