Design and performance of tree-structured vector quantizers

This paper considers optimal vector quantizers which minimize the expected distortion subject to a cost such as the number of leaves (storage cost), the leaf entropy (lossless encoding rate), the expected depth (average quantization time), or the maximum depth (maximum quantization time). It analyzes the heuristic of successive partitioning, and develops a class of strategies subsuming most of those used in the past. Experimental results show that these strategies are more efficient than existing methods, and achieve comparable or better compression. The relationship among different cost functions is considered and ways of combining multiple cost constraints are proposed.<>