Facical Image Compression Based on Wavelet Transform and Vector Quantization
基于小波变换和矢量量化的人脸图象压缩

In the compression of some particular kinds of image sources, such as human face, vector quantization should naturally be considered to exploit its statistical properties. In this paper, a new vector quantization method in the wavelet transform domain is proposed. We use tree structure to organize coefficients. In each tree, nodes are pruned or retained according to their importance. The survived nodes are serialized to be a vector, which will be the input of vector quantization; a map indicating the positions of these nodes is also stored, which is to be used in decoding. It embeds together the principles of EZW and SPIHT, exploiting fully both the parent children dependencies and brother dependencies. In our algorithm's framework, SFQ's tree pruning algorithm can also be embedded to increase the PSNR of reconstructed images, though we simply choose the threshold pruning method to reduce complexity of algorithm. Using the proposed algorithm, the reconstructed facial image in very low bit rate(about 0 08bpp) is superior to that of EZW, SPIHT, SFQ in both perception and PSNR. The algorithm is very suitable for the compression of particular kinds of image sources.