Kavli Affiliate: Wei Gao
| First 5 Authors: Changhao Peng, Yuqi Ye, Wei Gao, ,
| Summary:
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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