一种基于小波的多光谱图像压缩方法

A Wavelet-based Approach for Compression of Multi-spectral Images

文章提出一种基于Karhunen Loeve变换(KLT)和小波量化编码的多光谱图像压缩方法。该法首先使用KL变换步骤来去除谱间冗余,而后对各变换波段图像进行小波变换,并利用均匀阈值网格编码量化方法来量化小波子带图像,最后使用算术编码对量化结果进行熵编码。为使编码器能为所有谱段各子带获取率-失真意义上最优的量化阈值,本文提出基于子带图像统计特性和网格编码量化器率-失真特性的比特分配算法。实验表明,本方法能高效地压缩多光谱图像,表现出优异的压缩性能。

An approach for compression of multi-spectral images is proposed based on Karhunen-Loeve transform and wavelet trellis-coded quantization. Firstly, the algorithm takes advantage of the step of K-L transform to remove the spectral redundancy. Then the discrete wavelet transform is carried out over the image of K-L transformed results,and trellis-coded quantization with uniform threshold is adopted to quantize the sub-band images. At last, adaptive arithmetic encoding is adopted to entropy encode the quantized results. To compute optimal quantization thresholds in rate-distortion sense for each sub-bands at all spectral bands, an algorithm for bit allocation based on sub-band statistic characteristic and R-D characteristic of trellis-coded quantization is proposed. The experiments show that the approach can efficiently compress multi-spectral remote sensing images, and the excellent performance of the proposed algorithm is demonstrated.