Flexible Multiple Description Lattice Vector Quantizer with General Number of Descriptions

Abstract

This thesis addresses the design of multiple description lattice vector quantizer (MDLVQ) with a general number L of descriptions, L >= 3. In the previous work on MDLVQ with L>= 3, once the central and side lattice codebooks are fixed, the decoding quality is determined for all numbers k of received descriptions. Therefore, it is not possible to achieve tradeoffs between the quality of reconstruction for different values of k, 1<= k <= L-1.

In order to overcome the above drawback, we propose two flexible MDLVQ schemes for L >= 3. Our first design employs a different reconstruction method than in prior work and a heuristic index assignment algorithm, which uses L-2 parameters to control the distortions for 2 <= k <= L-1. Experimental results for the cases L=3 and L=4 show that significant tradeoffs are achieved by controlling the parameters mentioned above. Our second design is based on a structured index assignment. We start with the case L=3 and then generalize the index assignment to any L >= 3. The structured index assignment is able to control the tradeoff by adjusting the sizes of some L-1 subsets of side lattice points. Another important contribution of the thesis is the derivation of analytical expressions of the distortions for the structured index assignment, under the high resolution assumption. These expressions show that a wide range of distortion values can be achieved.