Multi-spectral image compression based on Tucker decomposition in integer wavelet domain

In order to solve the problems of destroyed intrinsic structure of image, information loss, and low compression efficiency which are produced by using the image as a matrix for compression, a multi-spectral image compression algorithm based on nonnegative tensor Tucker decomposition is proposed in this paper. Firstly, every band of the multi-spectral image is decomposed by 2D lifting-based 5/3 discrete integer wavelet transform (DWT) to reduce the space redundancy. Then the four sub-bands of each level DWT for the whole spectral coverage are used as four nonnegative tensors. And each nonnegative sub-band tensor is decomposed by the proposed improved local HALS-NTD algorithm to reduce the spectra redundancy and the residual space redundancy. Finally, the core tensor and factorization matrix are encoded by an entropy coder. The experiment results show that the proposed compression algorithm has good compressive property. In the compression ration range from 32:1 to 4:1, the average peak signal to noise ratio (SNR) of the proposed compression algorithm is higher than 40 dB. Compared with traditional approaches, the proposed method could improve the average PSNR by 1.939 dB. It effectively improves the compression performance of multi-spectral images.