An Optimization Algorithm for Scalable Multiple Description Scalar Quantizers

A scalable multiple description scalar quantizer (SMDSQ) is a quantization based framework used for scalable multiple description coding (SMDC). In this paper, we introduce a novel generalization of the Lloyd-Max algorithm to realize locally optimal SMDSQs. Both level-constrained and entropy-constrained cases are considered. For both cases, locally optimal solutions are realized by iterative execution of the centroid and the modified nearest-neighbor conditions. Experimental results confirm that, for a zero-mean unit-variance Gaussian source, the optimization algorithm enables a significant reduction in distortion for the level-constrained case. Moreover, relatively lesser but still significant distortion-rate (D-R) gains are viable for the entropy-constrained case. It is shown that, for a packetized transmission of Gaussian as well as wavelet-decomposed images, the obtained optimization gains translate into an average improvement in the decoder's signal-to-noise-ratio (SNR) for a wide range of packet loss rates.