This paper presents a new vector quantization technique called predict
ive residual vector quantization (PRVQ). It combines the concepts of p
redictive vector quantization (PVQ) and residual vector quantization (
RVQ) to implement a high performance VQ scheme with low search complex
ity. The proposed PRVQ consists of a vector predictor, designed by a m
ultilayer perceptron, and an RVQ that is designed by a multilayer comp
etitive neural network, A major task in our proposed PRVQ design is th
e joint optimization of the vector predictor and the RVQ codebooks, In
order to achieve this, a new design based on the neural network learn
ing algorithm is introduced. This technique is basically a nonlinear c
onstrained optimization where each constituent component of the PRVQ s
cheme is optimized by minimizing an appropriate stage error function w
ith a constraint on the overall error, This technique makes use of a L
agrangian formulation and iteratively solves a Lagrangian error functi
on to obtain a locally optimal solution. This approach is then compare
d to a jointly designed and a closed-loop design approach, In the join
tly designed approach, the predictor and quantizers are jointly optimi
zed by minimizing only the overall error, In the closed-loop design, h
owever, a predictor is first implemented; then the stage quantizers ar
e optimized for this predictor in a stage-by-stage fashion, Simulation
results show that the proposed PRVQ scheme outperforms the equivalent
RVQ (operating at the same bit rate) and the unconstrained VQ by 2 an
d 1.7 dB, respectively, Furthermore, the proposed PRVQ outperforms the
PVQ in the rate-distortion sense with significantly lower codebook se
arch complexity.