Deformable 2D Gaussian Splatting for Efficient 4K Video Compression

• URL: http://arxiv.org/abs/2609.14129v1
• Date: Sat, 12 Sep 2026 20:18:25 GMT
• Title: Deformable 2D Gaussian Splatting for Efficient 4K Video Compression
• Abstract summary: We propose a real-time video compression framework that represents and compresses a Group of Pictures (GOP)<n>Experiments demonstrate that our method delivers rate-distortion performance in LPIPS that surpasses H.265.<n>Our work demonstrates the potential of Gaussian Splatting as a practical solution for efficient high-resolution video compression.
• Score: 11.054100502251975
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Ultra-High-Definition (UHD) video presents significant challenges for efficient storage and real-time decoding. Learning-based methods, such as Neural Video Compression (NVC) and Implicit Neural Representations (INR), achieve competitive rate-distortion performance but suffer from high decoding latency and excessive memory usage. Meanwhile, Gaussian Splatting has recently attracted attention in the computer graphics community due to its ultra-fast rendering and high-fidelity visual quality. Despite these advantages, its application in video compression remains largely unexplored. To bridge this gap, we propose a real-time video compression framework that represents and compresses a Group of Pictures (GOP) using a coarse-to-fine multi-scale 2D Gaussian Splatting (2DGS) structure coupled with a lightweight deformation network. Experiments demonstrate that our method delivers rate-distortion performance in LPIPS that surpasses H.265 and other state-of-the-art learning-based video compression methods. Our work demonstrates the potential of Gaussian Splatting as a practical solution for efficient high-resolution video compression.

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