Optimized Subvector Processing in Split Vector Quantization

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Miguel Arjona Ramírez


Split vector quantization (SVQ) is efficient but suboptimal. Here a renormalization process is proposed for intraframe splitting and joining of subvectors, which integrates gracefully with trained interframe prediction. Renormalization increases the availability of codevectors for the quantization of each subvector in ordered vectors such as the line spectral frequency (LSF) vectors. For 16-dimensional LSF vectors from wideband speech, renormalized SVQ (RSVQ) is shown to achieve a savings of 4 bit/frame over standard SVQ, reaching transparent coding at 42 bit/frame. Further, predictive RSVQ saves an additional 4 bit/frame for transparent coding down to 38 bit/frame.

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Arjona Ramírez, M. (2015). Optimized Subvector Processing in Split Vector Quantization. Journal of Communication and Information Systems, 25(1). https://doi.org/10.14209/jcis.2010.3
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