可变区域矢量量化,空间翘曲和语音/图像压缩

Y. Matsuyama
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引用次数: 7

摘要

给出了变域数据的矢量量化算法。结果表明,设计迭代是收敛的。这里的一个重要问题是区域形状相对于矢量量化码本的优化步骤。因此,所提出的设计方法是普通矢量量化器设计的非平凡扩展,其中包含经典的Lloyd-Max算法。首先,在不引入任何物理实体的情况下给出了主要算法。因此,该方法适用于包括语音和图像在内的任何数据,只要定义了量化失真。在本文的主体语音编码中,将区域形状优化解释为epoch区间调整。相对于矢量量化码本的调整epoch的选择大大减少了量化失真。这可以实现非常低速率的语音压缩。然后,给出了图像编码的具体情况,并讨论了一些收敛问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variable region vector quantization, space warping and speech/Image compression
Algorithms for vector quantization of variable region data are given. The design iteration is proved to converge. An important issue here is the optimization step of the region shape with respect to the vector quantization codebook. Thus, the presented design method is a nontrivial extention of ordinary vector quantizer design which contains the classical Lloyd-Max algorithm. First, the main algorithm is given without introducing any physical entity. Therefore, the method is applicable to any data including speech and image as long as the quantization distortion is defined. In the speech coding case, which is the main body of this paper, the region shape optimization is interpreted as the epoch interval adjustment. The selection of the adjusted epochs with respect to the vector quantization codebook considerably reduces the quantizing distortion. This enables very-low-rate speech compression. Then, the image coding case is formulated and some convergence problem is discussed.
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