Implicit neural representations (INRs) recently achieved great success in image representation and compression, offering high visual quality and fast rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are available. However, this requirement often hinders their use on low-end devices with limited memory. In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage. We first introduce 2D Gaussian to represent the image, where each Gaussian has 8 parameters including position, covariance and color. Subsequently, we unveil a novel rendering algorithm based on accumulated summation. Remarkably, our method with a minimum of 3$\times$ lower GPU memory usage and 5$\times$ faster fitting time not only rivals INRs (e.g., WIRE, I-NGP) in representation performance, but also delivers a faster rendering speed of 1500-2000 FPS regardless of parameter size. Furthermore, we integrate existing vector quantization technique to build an image codec. Experimental results demonstrate that our codec attains rate-distortion performance comparable to compression-based INRs such as COIN and COIN++, while facilitating decoding speeds of approximately 1000 FPS. Additionally, preliminary proof of concept shows that our codec surpasses COIN and COIN++ in performance when using partial bits-back coding.

翻译：隐式神经表示（INRs）近期在图像表示与压缩领域取得了显著成功，凭借10-1000 FPS的渲染速度和高质量视觉效果（假设具备充足GPU资源）。然而，该GPU需求往往阻碍其在内存受限的低端设备上的应用。为此，我们提出了一种基于2D高斯泼溅的图像表示与压缩突破性范式——GaussianImage。首先引入2D高斯分布表示图像，每个高斯由8个参数（位置、协方差及颜色）构成。随后，我们提出一种基于累加求和的新型渲染算法。值得注意的是，本方法不仅以最低3倍GPU内存占用和5倍拟合速度优势在表示性能上媲美INRs（如WIRE、I-NGP），更能在参数规模无关的情况下实现1500-2000 FPS的快速渲染。进一步地，我们整合现有向量量化技术构建图像编解码器。实验表明，该编解码器在实现约1000 FPS解码速度的同时，达到了与COIN、COIN++等基于压缩的INRs相媲美的率失真性能。此外，初步概念验证显示，采用部分比特回退编码时，本编解码器在性能上超越COIN与COIN++。