This paper presents a new technique for designing a jointly optimized Multi-stage Vector Quantizer which is also known as the Residual Vector Quantizer (RVQ). In conventional stage-by-stage design procedure, each stage codebook is optimized for that particular stage distortion and does not consider the distortion from the subsequent stages. However, the overall performance can be improved if each stage codebook is optimized by minimizing the distortion from the subsequent stage quantizers as well as the distortion from the previous stage quantizers. This can only be achieved when stage codebooks are jointly designed for each other. In this paper, the proposed codebook design procedure is based on a multi-layer competitive neural network where each layer of this network represents one stage of the RVQ. The weight connecting these layers form the corresponding stage codebooks of the RVQ. The joint design problem of the RVQ's codebooks is formulated as a nonlinearly constrained optimization task which is based on a Lagrangian error function. The proposed procedure seeks a locally optimal solution by iteratively solving these equations for this Lagrangian error function. Simulation results show an improvement in the performance of an RVQ when designed using the proposed joint optimization technique as compared to the stage-by-stage design, where both Generalized Lloyd Algorithm and the Kohonen Learning Algorithm were used to design each stage codebook independently, as well as the conventional joint- optimization technique.

Date Published: 16 September 1994
PDF: 15 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185900

Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)