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 2000 FPS. Additionally, preliminary proof of concept shows that our codec surpasses COIN and COIN++ in performance when using partial bits-back coding. Code is available at https://github.com/Xinjie-Q/GaussianImage.

翻译：隐式神经表示（INRs）近期在图像表示与压缩领域取得了巨大成功，在GPU资源充足的假设下，能够提供高视觉质量与10-1000 FPS的快速渲染速度。然而，这一要求往往限制了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二维高斯泼溅的图像表示与压缩突破性范式，命名为GaussianImage。我们首先引入二维高斯函数来表示图像，其中每个高斯函数包含位置、协方差和颜色共8个参数。随后，我们提出了一种基于累加求和的新型渲染算法。值得注意的是，我们的方法在GPU内存使用量降低至少3倍、拟合时间加快5倍的同时，不仅在表示性能上可与INRs（如WIRE、I-NGP）相媲美，而且无论参数规模大小均能实现1500-2000 FPS的更快速渲染。此外，我们整合现有向量量化技术构建了图像编解码器。实验结果表明，我们的编解码器在率失真性能上达到了与COIN、COIN++等基于压缩的INRs相当的水平，同时支持约2000 FPS的解码速度。初步概念验证还显示，在使用部分比特回传编码时，我们的编解码器性能超越了COIN与COIN++。代码发布于https://github.com/Xinjie-Q/GaussianImage。