GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
arxiv(2024)
摘要
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× lower GPU memory usage and 5× 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.
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