A new approach to image compression using vector quantization of wavelet coefficients

Traditional image coding methods, such as vector quantization (VQ), discrete cosine transform (DCT) based coding, and entropy coding of subband, have been designed to eliminate statistical redundancy within still images. In this paper, a combined approach utilizing both transform coding and vector quantization techniques is used, hoping to achieve the best result in terms of compression ratio with acceptable recovery quality. The transform coding used is 2-D wavelet transform and the key is to tap the correlation between wavelet coefficients of different subbands in the same spatial location rather than only in the same orientation. Performance comparisons are made with three other VQ-based compression models. The result shows the strength of this novel approach in that it has the best reconstructed image quality in terms of its signal to noise ratio for a fixed compression ratio.