Fast Vector Quantization Algorithm for Hyperspectral Image Compression

Abstract

Vector Quantization (VQ) is widely used for Hyper Spectral Image (HSI) compression and VQ based algorithms yield good results for reducing the amount of the data. However, the VQ based algorithms have the shortcoming of computing expensive. Many fast VQ algorithms have been proposed to reduce the computing complexity, while the algorithms consider the HSI feature rarely. We present a new framework of fast vector quantization for HSI compression, which uses the spectra characteristics of HSI adequately. The breakthrough codebook training method is calculated at the HSI feature domain, which is a low dimension structure without losing significant information, to get much lower complexity. The experimental results demonstrate that the proposed algorithms can reduce the computing time dramatically while keep the comparable reconstruction fidelity.