Topological surgery encoding improvements based on adaptive bit allocationand DFSVQ

In this paper, new methods to improve the encoding of connectivity and geom etry of the topological surgery scheme are proposed. In connectivity compre ssion, after obtaining the vertex and triangle spanning trees by decomposin g a three-dimensional object, bits are adaptively allocated to each run of two spanning trees on a threshold basis. The threshold is the length of a b inary number of the maximum run length. if a run length exceeds the thresho ld, it is represented by a binary number of the run length. Otherwise, it i s represented by a bit sequence. Therefore, compression efficiency is enhan ced through an adaptive bit allocation to each run of two spanning trees. In geometry compression, since vertices represented by three-dimensional ve ctors are stored according to the order of the travelling along vertex span ning tree by depth-first searching, they have geometrical closeness. The ge ometry compression efficiency can be improved if the local characteristics of vectors are considered. Therefore, dynamic finite state vector quantizat ion, 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 distor tion and gives better visual quality than conventional methods.