Adaptive parametric vector quantization by natural type selection
Adaptive parametric vector quantization by natural type selection
复制标题
通过自然类型选择的自适应参数矢量量化
DOI：
10.1109/dcc.2002.999979
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
R. Zamir
中科院分区：
文献类型：
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作者：
Yuval Kochman;R. Zamir
我们提出了一种新的自适应机制，用于矢量量化码本的经验“在线”设计。所提出的方案基于“自然类型选择”（NTS）的原理（Zamir and Rose，2001）。 NTS原理意味着向后的适应，即由过去的重建而不是由未编码的源序列收敛到最佳费率 - 依学码书籍的适应性。我们将NTS迭代步骤结合到参数编码器中。我们证明，代码簿在关联的参数类中收敛到最佳利率延伸解决方案。这种新方案在非参数溶液的高维度（如广义劳埃德算法（GLA））的高度上没有严重的复杂性。此外，与现有的参数自适应方案不同（例如，代码激发的线性预测（CELP）），即使对于低编码率，该方案也是最佳的。
We present a new adaptive mechanism for empirical "on-line" design of a vector quantizer codebook. The proposed scheme is based on the principle of "natural type selection" (NTS) (Zamir and Rose, 2001). The NTS principle implies that backward adaptation, i.e., adaptation directed by the past reconstruction rather than by the uncoded source sequence converges to an optimum rate-distortion codebook. We incorporate the NTS iteration step into a parametric encoder. We demonstrate that the codebook converges to an optimum rate-distortion solution within the associated parametric class. This new scheme does not suffer from the severe complexity at high dimensions of nonparametric solutions like the generalized Lloyd algorithm (GLA). Moreover, unlike existing parametric adaptive schemes (e.g., code-excited linear prediction (CELP)), this scheme is optimal even for low coding rates.