1. FAST DYNAMIC QUANTIZATION ALGORITHM FOR VECTOR MAP COMPRESSION Minjie Chen, Mantao Xu and Pasi Fränti University of Eastern Finland

2. Vector data, embrace a number of geographic information or objects such as waypoints, routes and areas. It is represented with a sequence of points in a given coordinate system. In order to save storage cost, compression algorithm for vector data is needed. Map of UK GPS traces

3. Reduce the number of points in the vector map such that the data is represented in a coarser resolution. (Douglas73’,Perez94’,Schuster 98’, Bhowmick07’) Number of point is reduced from 10910 to 239

4. Reduce every points’ coding cost. The coordinate value is quantized and differential coordinates is encoded (Shekhar 02’, Akimov 04’) Given quantization level l, differential coordinates is quantized as: Coding Q (v i ) is equivalent to coding an integer vector q = ([Δx i /l], Δy i /l])

5. Integer vector q = ([Δx i /l], Δy i /l]) is encoded by probability distributions of q x and q y : Codebook itself must be encoded. But a large-sized codebook is intractable in order to achieve a desirable coding efficiency An intuitive solution is to adopt a single-parameter geometric distribution to model q x and q y : where p x, p y can be approximated by maximum likelihood estimation. Other solutions, uniform, negative binomial or Poisson distribution can also be considered

6. Example of using geometric distribution to estimate the probability (allocated coding bits) of q,for l = 0.0025 For ∆ xlFor ∆ yl

7. Suppose poly-line {p i,…,p j } is approximated by line segment, the approximation error can be defined as the sum of square distances from vertices p k (i≤k≤j): Poly-line {p i,…, p j } (black line) is approximated by (blue line )with approximating error The distortion can be calculated by: This can be calculated in O(1) time by [Perez 94’]

8. The distortion E is minimized under the constraint of bit constraint R: Dynamic quantization optimizes the cost function: Combine polygonal approximation and quantization-based method using dynamic programming. [Kolesnikov 05’]:

9. The minimization is solved by the shortest path search on a weighted directed acyclic graph (DAG) and dynamic programming. Suppose J i is the minimum weighting sum from p 1 to p i on G, A is an array used for backtracking operation, the recursive equation can be defined by:

10. Two parameters: Lagrangian parameter λ quantization level l Given one l, different λ → one rate-distortion curve Existing approach calculates many rate-distortion curves with different l and the best is the lower envelope of the set of curves. Rate-distortion curve for quantization step q k =0.01/2 k, k=0, 1/2,1,…, 5 Time-expensive

11. Proposed: if ∆x, ∆y follows geometric distribution or uniform distribution, by setting for each l, one optimal Lagrangian parameter λ is estimated as: black ‘+’: error balance principle red ‘o’: proposed Relationship between λ and l is derived, no need for multiple calculation of rate- distortion curve

12. Shortest path algorithm on a weighted DAG takes O(N 2 ) time. Incorporating a stop search criterion in DAG shortest path search The proposed method can also be applied for bit-rate constraint problem by several iterations using binary search on the quantization level l. Time complexity reduced as O(N 2 /M)

14. 128bits/point, original10 bits/point 5 bits/point 2 bits/point

15. CBC: clustering-based method RL: reference line method DQ: Dynamic quantization FDQ: Fast dynamic quantization

16. For geometric distribution For uniform distribution

17. Derivation for optimal Lagrangian multiplier λ for each quantization step l Fast dynamic quantization algorithm with O(N 2 /M) time complexity for lossy compression of vector data.

18. [Douglas 73’] D. H. Douglas, T. K. Peucker, "Algorithm for the reduction of the number of points required to represent a line or its caricature", The Canadian Cartographer, 10 (2), pp. 112-122, 1973. [Perez 94’] J. C. Perez, E. Vidal, "Optimum polygonal approximation of digitized curves", Pattern Recognition Letters, 15, 743–750, 1994. [Schuster 98’] G. M. Schuster and A. K. Katsaggelos, "An optimal polygonal boundary encoding scheme in the rate-distortion sense", IEEE Trans. on Image Processing, vol.7, pp. 13-26, 1998. [Bhowmick 07’] P. Bhowmick and B. Bhattacharya, "Fast polygonal approximation of digital curves using relaxed straightness properties", IEEE Trans. on PAMI, 29 (9), 1590-1602, 2007. [Shekhar 02’] S. Shekhar, S. Huang, Y. Djugash, J. Zhou, "Vector map compression: a clustering approach", 10th ACM Int. Symp.Advances in Geographic Inform, pp.74-80, 2002. [Akimov 04’] A. Akimov, A. Kolesnikov and P. Fränti, "Coordinate quantization in vector map compression", IASTED Conference on Visualization, Imaging and Image Processing (VIIP’04), pp. 748-753, 2004. [Kolesnikov 05’] A. Kolesnikov, "Optimal encoding of vector data with polygonal approximation and vertex quantization", SCIA’05, LNCS, vol. 3540, 1186–1195. 2005.