Wavelet based adaptive vector quantization for encoding large color images

The generation of multi-resolution codebooks for encoding and decoding large (2K×1K) color images is, in general, a challenging task, due to the long search processes involved. We have used an adaptive clustering technique for generating multi-resolution codebooks by vector quantization of wavelet-transformed sub-images up to four levels of decomposition in an efficient manner. Using a combination of clustering up to the fourth level and selective retention of sub-images at the first and second levels, images of varying qualities can be reconstructed from a multi-resolution codebook. The histograms of the reconstructed images demonstrate little or no change from the histogram of the original, even up to a compression ratio of 132:1. The peak signal-to-noise ratio and the mean square error are also better than those from other benchmark compression techniques