A Novel Classified Residual Dct for Hyperspectral Images Scalable Compression

Transform−based lossy compression has a huge potential for image compression. In this paper, we propose a scalable lossy compression algorithm using transform technology for hyperspectral images. The novelty of this paper lies in the classified residual DCT (Discrete Cosine Transform) as a spectral decorrelator. The classified residual DCT is an improvement of the traditional DCT, which makes the performance of DCT more close to the performance of KLT that is considered as the optimal transform for data compression in a statistical sense. After 2D wavelet transform in the spatial domain and classified residual DCT in the spectral domain, an appropriate 3D−SPIHT image coding scheme is applied to the transformed coefficients, which makes the bit stream have scalable property. Experiments show that our proposed algorithm is capable of providing a high compression performance.