Sa. Rizvi et Nm. Nasrabadi, RESIDUAL VECTOR QUANTIZATION USING A MULTILAYER COMPETITIVE NEURAL-NETWORK, IEEE journal on selected areas in communications, 12(9), 1994, pp. 1452-1459
This paper presents a new technique for designing a jointly optimized
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 s
tages, 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 qu
antizers. This can only be achieved when stage codebooks are jointly d
esigned for each other, In this paper, the proposed codebook design pr
ocedure is based on a multilayer competitive neural network where each
layer of this network represents one stage of the RVQ, The weight con
necting these layers form the corresponding stage codebooks of the RVQ
, The joint design problem of the RVQ's codebooks (weights of the mult
ilayer competitive neural network) is formulated as a nonlinearly cons
trained optimization task which is based on a Lagrangian error functio
n, This Lagrangian error function includes an the constraints that are
imposed by the joint optimization of the codebooks, The proposed proc
edure seeks a locally optimal solution by iteratively solving the equa
tions for this Lagrangian error function, Simulation results show an i
mprovement in the performance of an RVQ when designed using the propos
ed joint optimization technique as compared to the stage-by-stage desi
gn, where both generalized Lloyd algorithm (GLA) and the Kohonen learn
ing algorithm (KLA) were used to design each stage codebook independen
tly, as well as the conventional joint-optimization technique,