Clustered Codebook Quantization for 2D Gaussian-based Image Compression

2026-07-06

AI Summary

This work addresses the poor rate-distortion efficiency of high-fidelity 2D Gaussian image representations, which stems from their massive number of floating-point parameters. To tackle this issue, the authors propose a clustering-guided vector quantization method (CGVQ) that introduces, for the first time, a clustering-based grouping strategy to partition Gaussian parameters into homogeneous groups prior to quantization. This approach significantly reduces bitrate while preserving high reconstruction accuracy. Experimental results demonstrate that CGVQ achieves up to a 20% reduction in bitrate compared to existing baselines, while maintaining comparable visual quality, thereby substantially improving the compression efficiency of 2D Gaussian representations.

Abstract

Gaussian-based image representations effectively model image content using compact parametric primitives while preserving high visual fidelity, yet storing a large number of floating-point parameters per primitive degrades rate-distortion efficiency at higher fidelity targets. To improve the rate-distortion performance in Gaussian representation, we present our Cluster-Guided Vector Quantization (CGVQ), a Gaussian primitive based image compression method. Our key idea is to partition Gaussian parameters further into homogeneous groups prior to quantization, enabling higher compression efficiency and accurate parameter reconstruction. In practice, our extensive experiments show that CGVQ decreases the bpp by 20% with respect to our baseline, while maintaining on-par visual quality

Problem

• Gaussian-based image compression
• rate-distortion efficiency
• floating-point parameters
• image representation
• visual fidelity

Innovation

• Cluster-Guided Vector Quantization
• Gaussian-based image compression
• rate-distortion optimization
• parameter quantization
• homogeneous clustering