An efficient bit allocation for compressing normal meshes with an error-driven quantization

We propose a new wavelet compression algorithm based on the rate-distortion optimization for densely sampled triangular meshes. Exploiting the normal remesher of Guskov et al., the proposed algorithm includes a wavelet transform and an original bit allocation optimizing the quantization of the wavelet coefficients. The allocation process minimizes the reconstruction error for a given bit budget. As distortion measure, we use the mean square error of the normal mesh quantization, expressed according to the quantization error of each subband. We show that this metric is a suitable criterion to evaluate the reconstruction error, i.e., the geometric distance between the input mesh and the quantized normal one. Moreover, to design a fast bit allocation, we propose a model-based approach, depending on distribution of the wavelet coefficients. Compared to the state-of-the-art methods for normal meshes, our algorithm provides improvements in coding performance, up to +2.5dB compared to the original zerotree coder.