Design of entropy-constrained multiple-description scalar quantizers

Abstract-In this paper, we derive analytical expressions for the central and side quantizers which, under high-resolution assumptions, minimize the expected distortion of a symmetric multiple-description lattice vector quantization (MD-LVQ) system subject to entropy constraints on the side descriptions for given packet-loss probabilities. We consider a special case of the general -channel symmetric multiple-description problem where only a single parameter controls the redundancy tradeoffs between the central and the side distortions. Previous work on two-channel MD-LVQ showed that the distortions of the side quantizers can be expressed through the normalized second moment of a sphere. We show here that this is also the case for three-channel MD-LVQ. Furthermore, we conjecture that this is true for the general -channel MD-LVQ. For given source, target rate, and packet-loss probabilities we find the optimal number of descriptions and construct the MD-LVQ system that minimizes the expected distortion. We verify theoretical expressions by numerical simulations and show in a practical setup that significant performance improvements can be achieved over state-of-the-art two-channel MD-LVQ by using three-channel MD-LVQ.Index Terms-High-rate quantization, lattice quantization, multiple-description coding (MDC), vector quantization.

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confidence: 99%

n-channel entropy-constrained multiple-description lattice vector quantization

Østergaard

Jensen

Heusdens

2006

“…In [27], a design procedure for the construction of fixed-rate scalar quantizers was presented. In [29], that design procedure was extended to the entropy-constrained case. It is shown in [28] that at high rates, for the case of balanced descriptions (…”

Section: Code Constructionsmentioning

confidence: 99%

Optimal filter banks for multiple description coding: analysis and synthesis

Dragotti

Servetto

Vetterli

2002

Abstract-Multiple description (MD) coding is a source coding technique for information transmission over unreliable networks. In MD coding, the coder generates several different descriptions of the same signal and the decoder can produce a useful reconstruction of the source with any received subset of these descriptions. In this paper, we study the problem of MD coding of stationary Gaussian sources with memory. First, we compute an approximate MD rate distortion region for these sources, which we prove to be asymptotically tight at high rates. This region generalizes the MD rate distortion region of El Gamal, Cover, and Ozarow for memoryless Gaussian sources. Then, we develop an algorithm for the design of optimal two-channel biorthogonal filter banks for MD coding of Gaussian sources. We show that optimal filters are obtained by allocating the redundancy over frequency with a reverse "water-filling" strategy. Finally, we present experimental results which show the effectiveness of our filter banks in the low complexity, low rate regime.Index Terms-Filter bank design, integer-to-integer transforms, multiple description (MD) coding, rate distortion functions, robust source coding.

“…Examples of MD coding include locally optimal two-description fixed-rate multiple-description VQ (MDVQ) [21] and locally optimal two-description fixed-rate [22] and entropy-constrained [23] MDSQ. Since the submission of this paper, algorithms have been developed that achieve globally optimal VQ design for many of the systems studied here [24]; those algorithms rely directly on the optimal encoder and decoder definitions discussed in this work.…”

Section: B Network Vector Quantizers (Nvqs)mentioning

confidence: 99%

Network Vector Quantization

Fleming

Zhao

Effros

2004

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.