We address the issue of complexity for vectorquantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate-distortion (R/D)...
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We address the issue of complexity for vectorquantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate-distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity. but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods. (C) 2008 Elsevier Inc. All rights reserved.
The trellis-based scalar-vector quantizer (TB-SVQ) can achieve the rate-distortion performance bound for memoryless sources. This paper extends the scope of this quantizer to coding of sources viith memory. First cons...
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The trellis-based scalar-vector quantizer (TB-SVQ) can achieve the rate-distortion performance bound for memoryless sources. This paper extends the scope of this quantizer to coding of sources viith memory. First considered is a simple extension, called the predictive TB-SVQ, which applies a closed-loop predictive coding operation in each survivor path of the Viterbi codebook search algorithm, Although the predictive TB-SVQ outperforms all other known structured fixed-rate vector quantizers, due to practical reasons, it may not approach the rate-distortion Limit. A new quantization scheme motivated by the preceding idea of Laroia ft al,. called the preceded TB-SVQ, is also considered;the granular gain is realized by the underlying trellis code while the combination of the precoder and the SVQ structure pro,ides the boundary gain. This new quantization scheme is asymptotically optimal and can, in principle, approach the rate-distortion bound for Markov sources.
This article presents a method to reduce the complexity of trellis-based scalar-vector quantizers. The proposed method partitions the reproduction alphabet into equal-area regions and shapes the codebook subject to a ...
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This article presents a method to reduce the complexity of trellis-based scalar-vector quantizers. The proposed method partitions the reproduction alphabet into equal-area regions and shapes the codebook subject to a rate-reduced signaling of these regions, As the shaping bit rate reduces, the coder complexity can be significantly reduced without sacrificing any rate-distortion performance.
The well-known error propagation problem inherent in any variable-length coding operation limits the usefulness of variable-length encoded scalar quantizers for transmission over noisy channels. In the absence of chan...
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The well-known error propagation problem inherent in any variable-length coding operation limits the usefulness of variable-length encoded scalar quantizers for transmission over noisy channels. In the absence of channel noise however, these quantizers are known to perform better than error-minimizing fixed-rate Lloyd-Max quantizers for a wide class of memoryless sources. Motivated by this observation, a low complexity fixed-rate structuredvector quantizer for memoryless sources is described. This quantizer is referred to as the scalar-vector quantizer and the structure of its codebook is derived from a variable-length scalar quantizer. Design and implementation algorithms for this quantizer are developed and bounds on its performance are provided. The scalar-vector quantizer can be designed and implemented even for fine (high rate) quantization at relatively large block lengths and can achieve a rate-distortion performance superior to that of implementable LBG vector quantizers. Simulation results show that performance close to that of the optimal entropy-constrained scalar quantizer is possible with this fixed-rate quantizer. The scalar-vector quantizer is also robust against channel errors and outperforms both Lloyd-Max and entropy-constrained scalar quantizers for a wide range of channel error probabilities. These ideas are extended (in Part II) to the quantization of vector sources and, consequently, to sources with memory.
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