通过分组量化提升高保真图像压缩效率
Clustered Codebook Quantization for 2D Gaussian-based Image Compression
- 将高斯参数分组后分簇量化，提升压缩效率
- 相比基线降低20%码率，视觉质量相当
- 适合追求高保真图像压缩的场景
基于高斯的图像表示利用紧凑的参数化原型有效建模图像内容，同时保持高视觉保真度，但每个原型存储大量浮点参数会降低高保真目标下的率失真效率。为改善高斯表示的率失真性能，我们提出基于高斯原型的图像压缩方法——聚类引导向量量化（CGVQ）。核心思想是在量化前将高斯参数进一步划分为同质组，从而实现更高的压缩效率和精确的参数重建。实验表明，CGVQ相比基线在保持相当视觉质量的前提下，将码率（bpp）降低了20%。
原文摘要 · Abstract (English)
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
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