Topological surgery encoding improvements based on adaptive bit allocation and DFSVQ

In this paper, new methods to improve the encoding of connectivity and geometry of the topological surgery scheme are proposed. In connectivity compression, after obtaining the vertex and triangle spanning trees by decomposing a threedimensional object, bits are adaptively allocated to each run of two spanning trees on a threshold basis. The threshold is the length of a binary number of the maximum run length. If a run length exceeds the threshold, it is represented by a binary number of the run length. Otherwise, it is represented by a bit sequence. Therefore, compression efficiency is enhanced through an adaptive bit allocation to each run of two spanning trees. In geometry compression, since vertices represented by threedimensional vectors are stored according to the order of the travelling along vertex spanning tree by depth-first searching, they have geometrical closeness. The geometry compression efficiency can be improved if the local characteristics of vectors are considered. Therefore, dynamic finite state vector quantization, which has subcodebooks depending on a local characteristic of vectors, is used to encode the geometry information. As it dynamically constructs a subcodebook by predicting an input vector’s state, it produces less distortion and gives better visual quality than conventional methods.