Robust Predictive Quantization: Analysis and Design Via Convex Optimization

Predictive quantization is a simple and effective method for encoding slowly-varying signals that is widely used in speech and audio coding. It has been known qualitatively that leaving correlation in the encoded samples can lead to improved estimation at the decoder when encoded samples are subject...

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Vydáno v:IEEE journal of selected topics in signal processing Ročník 1; číslo 4; s. 618 - 632
Hlavní autoři: Fletcher, A.K., Rangan, S., Goyal, V.K., Ramchandran, K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2007
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Audio coding
Constraint optimization
Convex analysis
Decoding
Design optimization
Differential pulse code modulation
erasure channels
Filters
joint source-channel coding
Linear matrix inequalities
Linear systems
Quantization
Robustness
Speech coding
Studies
ISSN:1932-4553, 1941-0484
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Shrnutí:Predictive quantization is a simple and effective method for encoding slowly-varying signals that is widely used in speech and audio coding. It has been known qualitatively that leaving correlation in the encoded samples can lead to improved estimation at the decoder when encoded samples are subject to erasure. However, performance estimation in this case has required Monte Carlo simulation. Provided here is a novel method for efficiently computing the mean-squared error performance of a predictive quantization system with erasures via a convex optimization with linear matrix inequality constraints. The method is based on jump linear system modeling and applies to any autoregressive moving average (ARMA) signal source and any erasure channel described by an aperiodic and irreducible Markov chain. In addition to this quantification for a given encoder filter, a method is presented to design the encoder filter to minimize the reconstruction error. Optimization of the encoder filter is a nonconvex problem, but we are able to parameterize with a single scalar a set of encoder filters that yield low MSE. The design method reduces the prediction gain in the filter, leaving the redundancy in the signal for robustness. This illuminates the basic tradeoff between compression and robustness.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2007.910622