Optimality of Operator-Like Wavelets for Representing Sparse AR(1) Processes

The discrete cosine transform (DCT) is known to be asymptotically equivalent to the Karhunen-Loeve transform (KLT) of Gaussian first-order auto-regressive (AR(1)) processes. Since being uncorrelated under the Gaussian hypothesis is synonymous with independence, it also yields an independent- component analysis (ICA) of such signals. In this paper, we present a constructive non-Gaussian generalization of this result: the characterization of the optimal orthogonal transform (ICA) for the family of symmetric-alpha-stable AR(1) processes. The degree of sparsity of these processes is controlled by the stability parameter 0 < alpha <= 2with the only non-sparse member of the family being the classical Gaussian AR(1) process with alpha = 2. Specifically, we prove that, for alpha < 2, a fixed family of operator-like wavelet bases systematically outperforms the DCT in terms of compression and denoising ability. The effect is quantified with the help of two performance criteria (one based on the Kullback-Leibler divergence, and the other on Stein's formula for the minimum estimation error) that can also be viewed as statistical measures of independence. Finally, we observe that, for the sparser kind of processes with 0 < alpha <= 1, the operator-like wavelet basis, as dictated by linear system theory, is undistinguishable from the ICA solution obtained through numerical optimization. Our framework offers a unified view that encompasses sinusoidal transforms such as the DCT and a family of orthogonal Haar-like wavelets that is linked analytically to the underlying signal model.