改进点云属性压缩的熵模型，提升编码精度与压缩效率。
Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals
- 引入广义高斯熵模型，通过形状参数控制尾部形态以更准估计概率。
- 提出均值误差判别器动态调整区间，使整数编码更具适应性。
- 在三种基于VAE的模型上显著提升率失真性能，适用图像视频压缩。
高斯和拉普拉斯熵模型在学习型点云属性压缩中已被证明有效，有助于潜在变量的算术编码。然而，实验表明当前方法中神经网络估计的熵参数仍存在未利用信息，可进一步提升概率估计精度。为此，本文提出广义高斯熵模型，通过形状参数控制尾部形状，实现对潜在变量概率更精确的建模。同时，据我们所知，现有方法对每个整数使用固定似然区间进行算术编码，限制了模型性能。为此，我们设计均值误差判别器（MED），判断熵参数估计是否准确，并据此动态调整似然区间。实验表明，该方法在三种基于VAE的点云属性压缩模型上显著提升率失真（RD）性能，且可推广至图像、视频等其他压缩任务。
原文摘要 · Abstract (English)
Gaussian and Laplacian entropy models are proved effective in learned point cloud attribute compression, as they assist in arithmetic coding of latents. However, we demonstrate through experiments that there is still unutilized information in entropy parameters estimated by neural networks in current methods, which can be used for more accurate probability estimation. Thus we introduce generalized Gaussian entropy model, which controls the tail shape through shape parameter to more accurately estimate the probability of latents. Meanwhile, to the best of our knowledge, existing methods use fixed likelihood intervals for each integer during arithmetic coding, which limits model performance. We propose Mean Error Discriminator (MED) to determine whether the entropy parameter estimation is accurate and then dynamically adjust likelihood intervals. Experiments show that our method significantly improves rate-distortion (RD) performance on three VAE-based models for point cloud attribute compression, and our method can be applied to other compression tasks, such as image and video compression.
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