Tree-based search for ECVQ

Summary form only given. We propose two new tree-based search algorithms for vector quantizers using an additive weighted distance measure, such as ECVQ (entropy constrained vector quantization) (Chou et al., 1989). Both algorithms are based on a recursive space division technique, and use a bounding object at each node of the tree, in order to quickly eliminate subsets of the codebook during the search. The structure is more general than the k-d tree and the algorithm performs an optimal search similar to the one analyzed by Berchtold et al. (1997). We prove a theorem that defines the necessary and sufficient condition for any set of points to be a valid bounding object, i.e. to define a lossless pruning rule for the additive weighed Euclidean distance. The first algorithm presented uses rectangles as bounding objects, and the other uses spheres. We experimentally compare our approach with another recent one (Johnson et al., 1996), and show that the new algorithm using bounding rectangles performs significantly better for medium and high bitrate coding (>0.1 bits/sample) of a Gaussian process. This algorithm uses approximately 29 times less multiplications than a full codebook search at 1 bits/sample