基于聚类和小波变换的多光谱图像压缩算法

针对多光谱图像压缩算法现存的时空复杂度高、光谱特性利用不充分等问题，研究了多光谱图像的谱间稀疏等价表示及其聚类实现途径，进而设计了一种基于谱间自适应聚类和小波变换的多光谱图像压缩算法。算法利用吸引力传播聚类产生多光谱图像的谱间稀疏等价表示、在低复杂度下去除图像的谱间冗余，使用二维小波变换去除稀疏表示成分的空间冗余，采用分层树集合分割排序算法(SPIHT)进行压缩编码，并通过误差补偿机制提高多光谱图像重建质量。实验表明，该算法在保证较低时间和空间复杂度的基础上，较SPIHT等同类经典压缩算法，在相同的压缩比下，明显提高了重建图像的峰值信噪比，是一种通用有效的多光谱图像压缩算法。

Multispectral Image Compression Algorithm Based on Clustering and Wavelet Transform

Aiming at the problem of high time-space complexity and inadequate usage of spectral characteristics of existing multispectral image compression algorithms, an inter-spectrum sparse equivalent representation of multispectral image and its clustering realization ways were studied. Meanwhile, a new multispectral image compression algorithm based on spectral adaptive clustering and wavelet transform was designed. The affinity propagation clustering was utilized to generate inter-spectrum sparse equivalent representation which can remove inter-spectrum redundancy under low complexity, two-dimensional wavelet transform was used to remove spatial redundancy, and set partitioning in hierarchical trees (SPIHT) was used to encode. The quality of reconstruction images was improved by error compensation mechanism. Experimental results show that the proposed approach achieves good performance in time-space complexity, the peak signal-to-noise ratio(PSNR) is significantly higher than that of similar compression algorithms under the same compression ratio, and it is a generic and effective algorithm.