The post discusses Clustered Codebook Quantization (CGVQ), a novel method for image compression using Gaussian-based representations. It highlights the challenges of preserving visual fidelity while reducing the floating-point parameters required for storage. The author presents CGVQ, which partitions Gaussian parameters into homogeneous groups to enhance compression efficiency while maintaining visual quality. Experimental results indicate a 20% reduction in bits per pixel compared to the baseline, showing the method's effectiveness in improving rate-distortion performance.