Spectrum-Adaptive Distribution of 2D Gaussians for Image Representation and Compression

The prevailing Gaussian Splatting has been adapted for image representation and compression by GaussianImage recently, achieving impressive rendering speeds of 1500–2000 FPS. However, it falls short in rate-distortion performance compared to current state-of-the-art image codecs. We attribute this to its explicit representation and tile-based rasterization process which demands a more efficient utilization of 2D Gaussians for improved performance. In this work, we propose a spectrum-adaptive distribution method to allocate Gaussians in alignment with the complexity of varying image regions. Additionally, we introduce a gradient-based growing strategy for Gaussians to further refine their distribution. Experimental results show that our approach outperforms GaussianImage in image representation quality while maintaining comparable training and rendering speeds. Moreover, by integrating a transform-quantization pipeline for the position attributes of Gaussians, our approach delivers a better rate-distortion curve compared to the INR-based codecs such as COIN and COIN++.