Memory-based vector quantization of LSF parameters by a power series approximation
Journal article, 2007

Abstract: In this paper, memory-based quantization is studied in detail. We propose a new framework, Power Series Quantization (PSQ), for memory-based quantization. With LSF quantization as the application, several common memory-based quantization methods (FSVQ, predictive VQ, VPQ, safety-net etc.) are analyzed and compared with the proposed method, and it is shown that the proposed method performs better than all other tested methods. The proposed PSQ method is fully general, in that it can simulate all other memory-based quantizers if it is allowed unlimited complexity.

Author

Thomas Eriksson

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Fredrik Nordén

Chalmers, Signals and Systems, Information theory

IEEE Transactions on Audio, Speech and Language Processing

Vol. 15 4

Subject Categories

Signal Processing

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Created

10/8/2017