一种准无损压缩图象子带编码算法

针对有损压缩会损失一部分信息而无损压缩又压缩比较低的问题，提出一种实现准无损压缩的方法。该方法就是首先将图象用噪声模型进行去除噪声处理，以提高图象的信噪比，并有利于图象的压缩；然后再使用区域自适应子带编码算法进行编码。由于该算法能快速收敛，因而编码时间相对较少；编码通常能实时执行。实验结果表明，该压缩方案具有高信噪比、高压缩比等优良性能。从算法的理论基础来看，其中基于噪声模型的噪声清除算法对其他编码算法（如DCT、DPCM、JPEG、SPIHT、MPEG等等）同样具有推广意义。

A Nearly Lossless Subband Compression Algorithm

It is well known that there are a lot of noise data in many digital images. In generally speaking, the embedded noise data in an image are not only harmful to view the image, but also there is less correlation among noise data and original image data. So the noise data are hard to be compressed by general compression methods based on predictive or schotistic coding. Therefore a new method to achieve nearly lossless compression of an image is proposed in this paper. In the first step in the proposed method the noise data in an image are eliminated based on a noise model, and then the resulted image is as better as the original one for the usages. In the second step, the image is encoded by using a region adaptive subband compression algorithm without loss any data. Obviously, after decoding, the reconstruction image is a nearly lossless image without harmful to the usages. Coding time of the proposed algorithm is affordable thanks to fast convergence of the algorithm. Coding could always be performed in real time. The experimental result shows that the compression scheme provides impressive performance such as high signal to ratio and high compression ratio. According to the theory of the algorithm, the noise elimination algorithm based on noise model can also be extended to the other compression algorithms such as DCT, DPCM, JPEG, SPIHT and MPEG.