An Algorithm for the Design of Labeled-Transition Finite-State Vector Quantizers

A finite-state vector quantizer (FSVQ) is a switched vector quantizer where the sequence of quantizers selected by the encoder can be tracked by the decoder. It can be viewed as an adaptive vector quantizer with backward estimation, a vector generalization of an AQB system. Recently a family of algo...

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Bibliographic Details
Published in:I.R.E. transactions on communications systems 1985-01, Vol.33 (1), p.83-89
Main Authors: Dunham, M., Gray, R.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Applied sciences
Current measurement
Decoding
Distortion measurement
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Information, signal and communications theory
Iterative algorithms
Linear predictive coding
Read only memory
Speech
Stochastic processes
Telecommunications and information theory
Theoretical study. Circuits analysis and design
Vectors
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Summary:A finite-state vector quantizer (FSVQ) is a switched vector quantizer where the sequence of quantizers selected by the encoder can be tracked by the decoder. It can be viewed as an adaptive vector quantizer with backward estimation, a vector generalization of an AQB system. Recently a family of algorithms for the design of FSVQ's for waveform coding application has been introduced. These algorithms first design an initial set of vector quantizers together with a next-state function giving the rule by which the next quantizer is selected. The codebooks of this initial FSVQ are then iteratively improved by a natural extension of the usual memoryless vector quantizer design algorithm. The next-state function, however, is not modified from its initial form. In this paper we present two extensions of the FSVQ design algorithms. First, the algorithm for FSVQ design for waveform coders is extended to FSVQ design of linear predictive coded (LPC) speech parameter vectors using an Itakura-Saito distortion measure. Second, we introduce a new technique for the iterative improvement of the next-state function based on an algorithm from adaptive stochastic automata theory. The design algorithms are simulated for an LPC FSVQ and the results are compared with each other and to ordinary memoryless vector quantization. Several open problems suggested by the simulation results are presented.
ISSN:0090-6778
0096-2244
1558-0857
DOI:10.1109/TCOM.1985.1096198