基于混合编码和IWT的医学图像近无损压缩

Medical Image Compression Technique Based on Mixing Code and IWT

本文对医学图像先采用DPCM预测变换后,再选择IWT(整数小波变换)对其进行分解,对分解后的低频和高频子带分别作无损Huffman编码和有损矢量量化。根据小波分解后系数的分布特征,能量大部分集中在低频部分,对低频进行无损熵编码,对高频采用量化处理,去除人眼不敏感的冗余信息。最后利用处理过的低频和高频系数进行重构获得压缩后的图像。并与传统的离散小波变换压缩编码,JPEG和JPEG2000进行比较,实验结果表明,利用该方法能得到较高的压缩比和较好的压缩效果。

First DPCM forecast transformation was implemented to a medical image, and then integer wavelet transform was done to the image. The low frequency coefficients and high frequency coefficients were processed by Huffman code and vector quantification respectively. According to the properties of the wavelet coefficients, most of the energy was focused on the low frequency part. Lossless entropy code was implemented to the low frequency coefficients, and vector quantification was taken to the high frequency ones in order to reduce the redundant information, which was insensitive to person eyes. Finally, the processed low frequency and high frequency coefficients were reconstructed to obtain the compressed image. Compared with the compressions based on Discrete Wavelet Transformation （DWT）, JPEG and JPEG2000, experimental results indicate that a high compression ratio and the good compression effect can be obtained by the proposed method.