Optimal Design of Multiple Description Lattice Vector Quantizers

TL;DR

This paper introduces a linear-time, asymptotically optimal index assignment algorithm for multiple description lattice vector quantizers, improving design understanding and providing new distortion formulas for balanced descriptions.

Contribution

It proposes a novel greedy index assignment algorithm based on K-fraction lattices, proven to be asymptotically optimal for any K >= 2 in any dimension, with special results for K=2.

Findings

Algorithm is asymptotically optimal for large N

Closed-form distortion expression for K=2

Tighter asymptotic distortion formulas for K>2

Abstract

In the design of multiple description lattice vector quantizers (MDLVQ), index assignment plays a critical role. In addition, one also needs to choose the Voronoi cell size of the central lattice v, the sublattice index N, and the number of side descriptions K to minimize the expected MDLVQ distortion, given the total entropy rate of all side descriptions Rt and description loss probability p. In this paper we propose a linear-time MDLVQ index assignment algorithm for any K >= 2 balanced descriptions in any dimensions, based on a new construction of so-called K-fraction lattice. The algorithm is greedy in nature but is proven to be asymptotically (N -> infinity) optimal for any K >= 2 balanced descriptions in any dimensions, given Rt and p. The result is stronger when K = 2: the optimality holds for finite N as well, under some mild conditions. For K > 2, a local adjustment algorithm is developed to augment the greedy index assignment, and conjectured to be optimal for finite N. Our algorithmic study also leads to better understanding of v, N and K in optimal MDLVQ design. For K = 2 we derive, for the first time, a non-asymptotical closed form expression of the expected distortion of optimal MDLVQ in p, Rt, N. For K > 2, we tighten the current asymptotic formula of the expected distortion, relating the optimal values of N and K to p and Rt more precisely.