分组Karhun-Loeve变换/整数小波变换高光谱影像准无损压缩新方法

提出了分组Karhunen-Leove变换(KLT)和整数小波变换(IWT)的高光谱图像数据压缩方法,并采用整数小波变换技术和Set Partitioning in Hierarchical Trees(SPIHT)压缩编码,实现了对分组Karhun-Loeve变换后的数据压缩。该压缩编码方法与现有压缩方法相比,既保留了Karhun-Loeve变换压缩性能和整数小波变换高压缩比的特点,也宜于实时传输。实验结果表明,分组Karhun-Loeve变换/整数小波变换/SPIHT在相同压缩比下,峰值信噪比比Karhun-Loeve变换/小波变换/WSFCVQ、Karhun-Loeve变换/小波变换/改进的对块零树编码压缩和Karhun-Loeve变换/WT/FSVQ分别提高了6 dB,9 dB和8 dB,运算时间减少一半,整体压缩性能有了较大的提高。     

SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are introduced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional (1D) linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional (2D) integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ (WSFCVQ), the method of improved BiBlock zero tree coding (IBBZTC) and the method of feature spectral vector quantization (FSVQ), the peak signal-to-noise ratio (PSNR) of this method can enhance over 9 dB, and the total compression performance is improved greatly.