Entropy-coded quantization of periodic nonuniform samples

A. Mashiach, R. Zamir
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引用次数: 2

Abstract

We consider the quantization-noise amplification problem, in nonuniform sampling of bandlimited signals at Nyquist rate or higher. We first show that it is possible to avoid noise amplification in periodic nonuniform sampling, by oversampling (above Nyquist) by a factor of the period length. We prove that entropy-coded dithered quantization (ECDQ), with such periodic oversampling, can achieve the rate-distortion function (RDF) of a white Gaussian source. In the Nyquist (average) rate case, we show that while nonuniform sampling amplifies the reconstruction error, it lowers the ECDQ coding rate (though not enough to achieve the Gaussian RDF). We then show that if the ECDQ shapes the spectrum of the quantization noise via a feedback loop (which depends on the sampling pattern), then it can eliminate the noise amplification and achieve the Gaussian RDF.
周期性非均匀样本的熵编码量化
我们考虑了在奈奎斯特率或更高的带限信号的非均匀采样中的量化噪声放大问题。我们首先表明,在周期性非均匀采样中,通过周期长度因子的过采样(Nyquist以上),可以避免噪声放大。我们证明了采用这种周期性过采样的熵编码抖动量化(ECDQ)可以实现高斯白信源的率失真函数(RDF)。在奈奎斯特(平均)率的情况下,我们表明,虽然非均匀采样放大了重构误差,但它降低了ECDQ编码率(尽管不足以实现高斯RDF)。然后我们表明,如果ECDQ通过反馈回路(取决于采样模式)形成量化噪声的频谱,那么它可以消除噪声放大并实现高斯RDF。
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