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

【摘要】 <正>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.更多还原

【Abstract】 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.更多还原