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High-resolution source coding for non-difference distortion measures: multidimensional companding

机译:用于无差异失真测量的高分辨率源编码:多维压缩

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Entropy-coded vector quantization is studied using high-resolution multidimensional companding over a class of nondifference distortion measures. For distortion measures which are "locally quadratic" a rigorous derivation of the asymptotic distortion and entropy-coded rate of multidimensional companders is given along with conditions for the optimal choice of the compressor function. This optimum compressor, when it exists, depends on the distortion measure but not on the source distribution. The rate-distortion performance of the companding scheme is studied using an asymptotic expression for the rate-distortion function which parallels the Shannon lower bound for difference distortion measures. It is proved that the high-resolution performance of the scheme is arbitrarily close to the rate-distortion limit for large quantizer dimensions if the compressor function and the lattice quantizer used in the companding scheme are optimal, extending an analogous statement for entropy-coded lattice quantization and MSE distortion. The companding approach is applied to obtain a high-resolution quantizing scheme for noisy sources.
机译:在一类非差分失真测度上,使用高分辨率多维压缩技术研究了熵编码的矢量量化。对于“局部二次方”的失真度量,给出了多维压缩扩展器的渐近失真和熵编码率的严格推导,以及压缩函数最佳选择的条件。这种最佳压缩器(如果存在)取决于失真的度量,而不取决于信号源的分布。使用渐近表达式表示了压扩方案的率失真性能,该函数的率失真函数与香农下界的差分失真度量平行。实践证明,如果压缩函数和点阵量化器中使用的压缩函数和点阵量化器最优,则该方案的高分辨率性能可任意接近大量化器尺寸的速率失真极限,从而扩展了熵编码点阵的类似表述。量化和MSE失真。应用压扩方法来获得用于噪声源的高分辨率量化方案。

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