Scaled hierarchical vector quantization

A new technique to compress image data is introduced. Based on hierarchical properties of the tree structure, appropriate features can be drawn from specific region resulting in variable blocksize partitioning. To simplify the operation, the proposed scheme applies a scaling process to the derived feature. This scaling is similar to normalization of input vector to a unified dimension thereby a single codebook is used. This arrangement reduces complexity of the vector quantization (VQ) process dramatically. As a single pass algorithm, this VQ which uses image data in 3 regular sizes requires a minimal overhead. The simulation results show that this method not only decreases the search time but improves the quality of reconstructed images at low bit rates as well