Fast dynamic quantization algorithm for vector map compression

Vector map compression can be solved by incorporating both data reduction (polygonal approximation) and quantization of the prediction errors, which is the so-called dynamic quantization. This straightforward solution is to calculate all the rate-distortion curves with respect to each of the quantization levels such that the best quantizer is the lower envelope of the set of curves. But computing an entire set of rate-distortion curves is computationally expensive. To solve this problem, we propose a fast algorithm first estimates an optimal Lagrangian parameter λ for each given quantization level l and thus only one rate-distortion curve is achievable for constructing the optimal quantizer of prediction errors. An experimental result demonstrates that proposed algorithm reduces the computational complexity significantly without compromising its rate-distortion performance.