Network vector quantization

Abstract

Abstract-We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples.

Distributed Video Coding

“…The quantizer divides the signal space into cells, which, however, may consist of noncontiguous subcells mapped into the same quantizer index . This setting was considered, e.g., by Fleming, Zhao and Effros [48], who generalized the Lloyd algorithm [49] for locally optimal fixed-rate Wyner-Ziv vector quantization design. Later, Fleming and Effros [50] included rate-distortion optimized vector quantizers in which the rate measure is a function of the quantization index, for example, a codeword length.…”

Design of Optimal Quantizers for Distributed Coding of Noisy Sources

“…A different approach was followed in [4], where the Lloyd algorithm was generalized for locally optimal, fixed-rate WZ quantization design. Later, [5] included rate-distortion optimized quantizers in which the rate measure is a function of the quantization index, for example, a codeword length. Lloyd quantization for ideal Slepian-Wolf coding without side information was considered in [6].…”

Optimal multiple description and multiresolution scalar quantizer design

“…1 Prior work on fixed-rate multiple description scalar quantizer design includes both iterative descent algorithms [1] and approaches that rely on shortest path algorithms [2], [3]. 2 Algorithms in the first family guarantee locally optimal code design and generalize easily from scalar to vector quantizers [5], [6]. Unfortunately there may exist multiple local optima, and the difference between the performance of the best and worst of these is unbounded; as a result, it is difficult to make strong, theoretical statements about the quality of solutions designed through iterative descent algorithms.…”