Compression of multicomponent satellite images using independent components analysis
Résumé
In this paper we propose some compression schemes for multicomponent satellite images. These compression schemes use a classical bi-dimensional discrete wavelet transform (DWT) for spatial redundancy reduction, associated with linear transforms that reduce the spectral redundancy in an optimal way, for uniform scalar quantizers. These transforms are returned by Independent Component Analysis (ICA) algorithms which have been modified in order to maximize the compression gain under the assumption of high rate quantization and entropy coding. One algorithm, called ICA_opt, returns an optimal asymptotical linear transform and the other, called ICA_orth, returns an optimal asymptotical orthogonal transform. We compare the performance in high and medium rate coding of the Karhunen Loeve Transform (KLT) with the transforms returned by the modified ICA algorithms. These last transforms perform better than the KLT in term of compression gain in all cases, and in some cases the gain becomes significant.