Multidimensional rotations for robust quantization of image data

Abstract

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.

Source coding with distortion side information at the encoder

“…One example is phase quantization where applications such as Magnetic Resonance Imaging, Synthetic Aperture Radar, and Ultrasonic Microscopy infer physical phenomena from the phase shifts induced in a probe signal [15], [16], [17]. Alternatively, when magnitude and phase information must both be recorded, there are sometimes advantages to treating these separately, [18], [19], [20], [21]. The key special case when only two phases are recorded corresponds to Hamming distortion and we use this scenario to illustrate how distortion side information affects quantization.…”