Image compression using wavelet packet transform and vector quantization
Abstract
The purpose of this paper is to introduce an image compression scheme using a combination of wavelet packet transform [1] and pyramidal vector quantization [2]. All the wavelet packet bases corresponding to various tree structures have been considered and the best one has been coined based upon the peak signal to noise ratio and compression ratio of the reconstructed image. In first step input image decorrelation using the wavelet packet transform, the second step in the coder construction is the design of a pyramid vector quantizer. Pyramid Vector Quantization (PVQ) was first introduced by Fischer [2] as a fast and efficient method of quantizing Laplacian-like data, such as generated by transforms (especially wavelet transforms) or sub-band filters in an image compression system. PVQ has very simple systematic encoding and decoding algorithms and does not require codebook storage. PVQ has culminated in high performance and faster PVQ image compression systems for both transforms and subband decompositions. The proposed algorithm provides a good compression performance.
