MULTIDIMENSIONAL ROTATIONS FOR ROBUST QUANTIZATION OF IMAGE DATA

Laplacian and generalized Gaussian data arise in the transform and sub band coding of images, This paper describes a method of rotating indep endent, identically distributed (i.i.d.) Laplacian-like data in multip le dimensions to Significantly improve the overload characteristics fo r quantization, The rotation is motivated by the geometry of the Lapla cian probability distribution, and can be achieved with only additions and subtractions using a Walsh-Hadamard transform, Its theoretical an d simulated results for scalar, lattice, and polar quantization are pr esented in this paper, followed by a direct application to image compr ession, We show that rotating the image data before quantization not o nly improves compression performance, but also increases robustness to the channel noise and deep fades often encountered in wireless commun ication.