Low-Complexity CU Partitioning for 3D-HEVC Depth Maps via SE Attention and Ensemble Learning

Low-Complexity CU Partitioning for 3D-HEVC Depth Maps via SE Attention and Ensemble Learning | 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 Low-Complexity CU Partitioning for 3D-HEVC Depth Maps via SE Attention and Ensemble Learning Erlin Tian, JiaBao Zhang, Qiuwen Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7021250/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 Efficient Coding Unit (CU) partitioning is critical to reducing the computational complexity of 3D High Efficiency Video Coding (3D-HEVC), especially for depth maps. While many existing methods rely on either handcrafted features or deep learning, they often suffer from limited feature expressiveness and insufficient focus on structurally significant regions. To address these limitations, we propose a symmetry-aware, two-stage CU partitioning framework. First, a Convolutional Neural Network (CNN) equipped with a channel-wise Squeeze-and-Excitation (SE) attention mechanism is employed to extract multi-scale texture features, highlighting symmetry-relevant patterns. These deep features are then unified via Spatial Pyramid Pooling (SPP) and combined with handcrafted descriptors—such as neighborhood RDcost, directional gradients, and variance—and fed into a Bagged Tree classifier for final prediction. Additionally, a weighted voting strategy is adopted to replace conventional majority voting in the ensemble, enhancing robustness near decision boundaries. Experimental results show that the proposed method achieves an average 52.49% reduction in encoding time, with only a 0.39% increase in Bjøntegaard delta bitrate (BDBR), achieving an excellent trade-off between complexity and performance. 3D-HEVC CU partitioning Depth map Computational complexity SE attention Full Text 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-7021250","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528647987,"identity":"61511aff-7f5f-49ba-b738-47caeee7c06c","order_by":0,"name":"Erlin Tian","email":"","orcid":"","institution":"Zhengzhou University of Light Industry","correspondingAuthor":false,"prefix":"","firstName":"Erlin","middleName":"","lastName":"Tian","suffix":""},{"id":528647990,"identity":"c21e1d94-51c4-4357-a3cd-793c7e86da1c","order_by":1,"name":"JiaBao 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While many existing methods rely on either handcrafted features or deep learning, they often suffer from limited feature expressiveness and insufficient focus on structurally significant regions. To address these limitations, we propose a symmetry-aware, two-stage CU partitioning framework. First, a Convolutional Neural Network (CNN) equipped with a channel-wise Squeeze-and-Excitation (SE) attention mechanism is employed to extract multi-scale texture features, highlighting symmetry-relevant patterns. These deep features are then unified via Spatial Pyramid Pooling (SPP) and combined with handcrafted descriptors\u0026mdash;such as neighborhood RDcost, directional gradients, and variance\u0026mdash;and fed into a Bagged Tree classifier for final prediction. Additionally, a weighted voting strategy is adopted to replace conventional majority voting in the ensemble, enhancing robustness near decision boundaries. 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