Still-image compression using CVQ and wavelet transform

Wavelet transform which provides a multiresolution representation of images has been widely used in image and video compression. An investigation of wavelet decomposition reveals the cross-correlation among subimages at different resolutions. To exploit this cross-correlation, a new scheme using classified vector quantization to encode wavelet coefficients is proposed in this paper. The original image is first decomposed into a hierarchy of three layers containing ten subimages by discrete wavelet transform. The lowest resolution (level 3) low frequency subimage is scalar quantized (with 8 bits/pixel) since it contains most of the energy of the wavelet coefficients. All high frequency subimages are vector quantized to utilize the cross-correlation among different resolutions. Vectors are constructed by combining the corresponding coefficients of the high frequency subimages of the same orientation at different resolutions. Classified vector quantization is used to reduce edge distortion and computational complexity. Computer simulations are carried out to evaluate the performance of the proposed method.