A scalable SPIHT-based multispectral image compression technique

This paper addresses the compression of multispectral images which can be viewed, at the encoder side, as a three-dimensional (3D) data set characterized by a high correlation through the successive bands. Recently, the celebrated 3D-SPIHT (Sets Partitioning In Hierarchical Trees) algorithm has been widely adopted in the literature for the coding of multispectral images because of its proven state-of-the art performance. In order to exploit the spectral redundancy in the 3D wavelet transform domain, a new scalable SPIHT based multispectral image compression technique is proposed. The rational behind this approach is that image components in two consecutive transformed bands are significantly dependent in terms of zerotrees locations in the 3D-DWT domain. Therefore, by joining the trees with the same location into the List of Insignificant Sets (LIS), a considerable amount of bits can be reduced in the sorting pass in comparison with the separate encoding of the transformed bands. Numerical experiments on two sample multispectral images show a highly better performance of the proposed technique when compared to the conventional 3D-SPIHT.