Multiple Description Quantization via Gram-Schmidt Orthogonalization
Jun Chen, Chao Tian, Toby Berger, Sheila Hemami

TL;DR
This paper introduces a systematic framework for multiple description quantization, leveraging Gram-Schmidt orthogonalization, to achieve optimal rate-distortion performance for Gaussian and other sources, with practical low-complexity schemes.
Contribution
It presents a new successive quantization scheme linked to Gram-Schmidt orthogonalization, achieving the entire Gaussian MD rate-distortion region and offering universal, high-performance quantization methods.
Findings
Achieves the entire Gaussian MD rate-distortion region with lattice-based quantization.
Provides a universal scheme applicable to all i.i.d. smooth sources.
Develops low-complexity scalar quantizers with improved high-resolution performance.
Abstract
The multiple description (MD) problem has received considerable attention as a model of information transmission over unreliable channels. A general framework for designing efficient multiple description quantization schemes is proposed in this paper. We provide a systematic treatment of the El Gamal-Cover (EGC) achievable MD rate-distortion region, and show that any point in the EGC region can be achieved via a successive quantization scheme along with quantization splitting. For the quadratic Gaussian case, the proposed scheme has an intrinsic connection with the Gram-Schmidt orthogonalization, which implies that the whole Gaussian MD rate-distortion region is achievable with a sequential dithered lattice-based quantization scheme as the dimension of the (optimal) lattice quantizers becomes large. Moreover, this scheme is shown to be universal for all i.i.d. smooth sources with…
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