3D Multispectral Imagery Data Compression Based on KLT/WT a nd VQ with Spectral-feature-coding
基于KLT/WT和谱特征矢量量化三维谱像数据压缩

In this paper we propose a new method for multispec tr al image data compression based on Karhunen-Leove Transformation (KLT)/Wavelet Transformation (WT) and VQ with Spectral Feature Coding(SFCVQ). After KLT is app lied to multispectral image data for exploiting the spectral correlation, the Wa velet Transformation (WT) is used for the transformed multispectral image data t o remove spatial redundancy. Then the SFCVQ is designed to compress every spectr al image data. A higher compression performance is obtained. Experimental result s shows that in comparison of the methods of KLT/WT/SFCVQ with KLT/KLT/VQ, under the condition of the same Compression Ratio ( CR ), the Peak Signal to Noise Ratio ( PSNR ) is not varied apparently, while the compression speed increase s 30folds, or 5folds compared with the KLT/WT/FSVQ, and the total compression pe rformance has a great enhancement.