Multidimensional rotations for robust quantization of image data

Laplacian and generalized Gaussian data arise in the transform and subband coding of images. This paper describes a method of rotating independent, identically distributed (i.i.d.) Laplacian-like data in multiple dimensions to significantly improve the overload characteristics for quantization. The rotation is motivated by the geometry of the Laplacian probability distribution, and can be achieved with only additions and subtractions using a Walsh–Hadamard transform. Its theoretical and simulated results for scalar, lattice, and polar quantization are presented in this paper, followed by a direct application to image compression. We show that rotating the image data before quantization not only improves compression performance, but also increases robustness to the channel noise and deep fades often encountered in wireless communication.