In this work, we explore 2D Gaussian splat as a new primitive for representing and compressing videos, showing its promise as an alternative to traditional signal-based primitives. We introduce GSVC, a new Gaussian splatting-based video codec that incorporates common techniques used in classical video codecs to exploit temporal redundancy between nearby frames. In GSVC, each frame is represented by a set of overlapping Gaussian splats, which, when rendered together, approximate the original frame. Gaussian splats for the first frame and subsequent frames following scene changes (called anchor frames) are learned from scratch, while the remaining frames (called propagation frames) are initialized from the Gaussian splats of their previous frames and then fine-tuned. Scene changes are detected through loss differences during the training process. To capture large motion or newly appeared objects, we randomly add Gaussian splats to propagation frames before fine-tuning. GSVC supports a knob for rate-distortion trade-offs through controlling the number of Gaussian splats for each frame. Gaussian splats with low contributions to the frame quality can be removed during training. GSVC supports two-pass encoding by propagating the Gaussian splats from a future propagation frame back to an anchor frame, further improving the video quality for challenging video scenes. To achieve efficient compression, GSVC uses a residual-based Gaussian splats quantization framework that encodes only changes to the Gaussian parameters between frames. Experimental results show that GSVC achieves rate-distortion performance comparable to state-of-the-art video codec implementations for AV1 and VVC. GSVC inherits the advantages of Gaussian splatting in rendering, allowing it to achieve the rendering speed of 1500 fps for a 1920 × 1080 video.
Visualizing the traced 9k Gaussians. Each scene uses one synchronized video viewer: two sliders reveal Rendered Output, Motion Vectors, and Gaussian Distribution.
GSVC can achieve a significant improvement over the I-frame only method (Ours-GI), which use GaussianImage to encode each frame independently, demonstrating the contributions of predictive frames, pruning, augmentation, and key-frame detections brought to the representation.
Notice the floating dots in the up-left video for I-frame only method.
The neural-based approach achieves significant improvements over GSVC and even some state-of-the-art codecs such as VVC in terms of MS-SSIM.
These neural-based approaches, rely on pre-trained models and control rate-quality through learned quantization parameters. In contrast, like standard MPEG/H.26x codecs, GSVC requires no pre-training and can be directly applied to unseen videos. Moreover, GSVC supports arbitrary Gaussian budgets, providing fine-grained control over the rate-distortion trade-off.
GSVC achieves better or comparable performance in terms of MS-SSIM and VMAF (which consider image structure and human perception) except VVC. Our experiments on GSVC yield a lower PSNR in certain scenarios. But, it is important to note that we use \(L_2\) as a loss function, and we can tune the convergence condition to further improve the PSNR, without increasing \(N\), if we sacrifice the encoding time.
@article{wang2025gsvc,
title={{GSVC}: Efficient Video Representation and Compression Through {2D Gaussian} Splatting},
author={Wang, Longan and Shi, Yuang and Ooi, Wei Tsang},
journal={arXiv preprint arXiv:2501.12060},
year={2025}
}