基于小波变换和矢量量化的人脸图象压缩

Facical Image Compression Based on Wavelet Transform and Vector Quantization

在图象的压缩编码中 ,矢量量化可以利用某特定类图象 (如人脸 )的统计特性 .为了在高压缩比下获得较好的压缩效果 ,提出了一种新的在小波变换域内进行矢量量化的算法 .该算法用树结构表示小波变换域系数 ,并根据各节点值的重要程度 ,从每一棵树中提取一个矢量 ,然后进行矢量量化 ;解码时 ,为了使矢量分量能正确地返回到原来树中的正确位置 ,需利用 EZW[1 ]、SPIHT[2 ]算法的思想标记这棵树 ,因为这样才能充分利用父子相关性和兄弟相关性 ,从而显著地减少了标记信息 .在提取矢量时 ,可用简单的阈值剪枝算法 ,也可用 SFQ[3]的最佳剪枝算法 ,而且后者能进一步提高峰值信噪比 .用该算法对人脸图象进行的压缩试验结果表明 ,在高压缩比 (10 0∶ 1左右 )下 ,恢复的图象质量 (视觉效果和峰值信噪比 )比通常的小波压缩算法 (如 EZW、SPIHT、SFQ等 )好得多 .

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.