一种基于网格编码量化的高光谱图像无损压缩方法

由于遥感图像的数据量非常庞大,给有限的存储空间和传输带宽带来很大的压力,同时,由于高光谱图像获取代价昂贵,具有广泛的应用领域,且压缩时一般不能丢失任何信息,即要求无损压缩,因此没有有效的压缩方法,高光谱图像的普及应用将受到极大的限制.网格编码量化（TCQ）借鉴了网格编码调制（TCM）中信号集合扩展、信号集合划分和网格状态转移的思想,其虽具有良好的均方误差（MSE）性能,而且计算复杂度适中,但目前TCQ主要被应用于图像的有损压缩,为了对高光谱图像进行有效的无损压缩,通过将TCQ引入高光谱图像的无损压缩,并根据高光谱图像的特点,提出了一种基于小波变换和TCQ的高光谱图像无损压缩方法.实验结果表明,与JPEG2000和JPEG-LS中无损压缩算法相比,该算法对高光谱图像具有更好的压缩性能.

A Lossless Hyperspectral Image Compression Method Based on Trellis-coded Quantization

Huge amounts of data of hyperspectral images have become a great challenge to data storage and transmission.Due to the special way to obtain hyperspectral images,they are costly,but this kind of image can be used extensively in the meantime.Any information shouldn't be lost during compression,so an efficient lossless compression method seems to be essential.Without an efficient compression scheme,the application of hyperspectral images will be restricted.Trellis coded quantization(TCQ) inherits ideas from trellis coded modulation(TCM) the expanded signal set,set partitioning and trellis state transition.The mean squared error(MSE) performance of TCQ is excellent with modest computing complexity.At present TCQ is mainly used for lossy image compression.In this paper,we aim to develop TCQ scheme to compress hyperspectral images losslessly and find a new approach yielding higher lossless compression ratio.We take advantage of the characteristics of hyperspectral data for compression.A scheme utilizing wavelet transformation and TCQ is presented.Compared with the result of JPEG2000 and JPEG-LS,experiments show that the method presented in this paper has better performance.