基于Karhunen-Loève变换和小波谱特征矢量量化的三维谱像数据压缩

提出了基于Karhunen Lo埁ｖｅ变换的小波谱特征矢量量化三维谱像数据压缩方法耍幔颍瑁酰睿澹?Lo埁ｖｅ变换 /小波变换 /小波谱特征矢量量化方法应用了Karhunen Lo埁ｖｅ变换的消除谱相关性优良性能 ,应用二维小波变换消除空间相关性 ,在小波变换域内应用二维集分割嵌入块编码和一维谱特征矢量量化对三维谱像数据压缩 ,获得较高的压缩性能。实验结果表明 :Karhunen Lo埁ｖｅ变换 /小波变换 /小波谱特征矢量量化编码比Karhunen Lo埁ｖｅ变换 /小波变换 /改进对块零树编码和Karhunen Lo埁ｖｅ变换 /小波变换 /快速矢量量化编码方法在同样压缩比条件下 ,峰值信噪比提高 2dB和 1dB以上 ,而速度提高了 1.5和 8倍 ,整体压缩性能有较大的提高

Three-Dimensional Multispectral Image Data Compression Based on Karhunen-Loève Transformation/Wavelet Transformation and Vector Quantification with Spectral Feature Coding

A new method for three-dimensional multispectral image data compression based on an improved Karhunen-Loève Transformation (K-LT)/wavelet transformation spectral feature coding vector quantification (WSFCVQ) is proposed. Three-dimensional Karhunen-Loève transformation/wavelet transformation/spectral feature coding vector quantification (SFCVQ) implement three-dimensional compression for three-dimensional multispectral image data with two-dimensional set partition embedded block (SPECK) and one-dimensional spectral feature vector quantification (SFVQ) coding, applying Karhunen-Loève transformation to exploit the spectral correlation in multispectral image data and using two-dimensional wavelet transformation (WT) to remove the spatial redundancy in the multispectral image data, and a high compression performance is got as well. Experimental results show: if Karhunen-Loève transformation/wavelet transformation/wavelet spectral feature coding vector quantification (WSFCVQ) method are compared with the method of Karhunen-Loeve transformation/wavelet transformation/improvement BiBlock zero tree coding (IBBZTC) and the method of Karhunen-Loeve transformation/wavelet transformation/fast speed vector quantification (FSVQ), the peak signal to noise ratio (PSNR) is enhanced by over 2 dB and 1 dB, the compression speed increased by 1.5 and 8 times respectively, and the total compression performance is greatly improved.