Optimized Entropy-constrained Vector Quantization of lossy Vector Map Compression

Quantization plays an important part in lossy vector map compression, for which the existing solutions are based on either a fixed size open-loop codebook, or a simple uniform quantization. In this paper, we proposed an entropy-constrained vector quantization to optimize both the structure and size of the codebook at the same time using a closed-loop approach. In order to lower the distortion to a desirable level, we exploit two-level design strategy, where the vector quantization codebook is designed only for most common vectors and the remaining (outlier) vectors are coded by uniform quantization.