论文标题
渐近尺度不变的多分辨率量化
Asymptotically Scale-invariant Multi-resolution Quantization
论文作者
论文摘要
多分辨率量化器是一系列量化器，其中可以从较好的量化器的输出中推导出更粗的量化器的输出。在本文中，我们提出了一个渐近不变的多分辨率量化器，当输入数的长度较大时，它在任何平均量化步骤的任何选择中都均匀地执行。比例不变性在最坏情况或对抗设置中特别有用，确保量化器的性能不会受到存储或错误要求的少量变化的极大影响。我们还表明，所提出的量化器在任意接近最佳的速率和误差之间实现了权衡。
A multi-resolution quantizer is a sequence of quantizers where the output of a coarser quantizer can be deduced from the output of a finer quantizer. In this paper, we propose an asymptotically scale-invariant multi-resolution quantizer, which performs uniformly across any choice of average quantization step, when the length of the range of input numbers is large. Scale invariance is especially useful in worst case or adversarial settings, ensuring that the performance of the quantizer would not be affected greatly by small changes of storage or error requirements. We also show that the proposed quantizer achieves a tradeoff between rate and error that is arbitrarily close to the optimum.
